V S Babu

Mahesh Padaki

Laveena P D'souza

Sébastien Déon

R Geetha Balakrishna

A F Ismail

Effect of hydraulic coefficient on membrane performance for rejection of emerging contaminants

The efficient removal of Endocrine Disruptive Chemicals (EDCs) namely oxybenzone and atrazine using poly-sulfone (PSf) composite membranes is reported in this article. A negatively charged hydrophilic mixed matrix membrane was prepared by using Polyaniline modified halloysite nanotubes (PANi-HNT) and polysulfone. The X-ray diffraction (XRD) and attenuated total reflectance infrared (ATR-IR) technique confirm the conversion of Halloysite nanotubes to Polyaniline modified halloysite nanotubes and their presence in membrane matrix. The microvoids observable in the SEM images depict the internal structure of the membrane. Further, increasing in the water uptake and decreasing in the contact angle with respect to increasing the concentration of Polyaniline modified halloysite nanotubes confirm the enhancement of the membrane hydrophilicity. This hydrophilicity increases for higher concentrations of PANi-HNT leads to a significant improvement of the water flux. The removal efficiency of prepared mixed matrix membranes was found to be 98% for oxybenzone and 50% for atrazine. The separation process was discussed in terms of adsorption coefficient, membrane charge and mean pore size. Considering the performances highlighted in this study, the proposed membranes appear usable for the removal of EDCs from contaminated solutions.

Introduction

The chemicals or natural by-products in the environment that mimic the hormones in the body are called Endocrine Disruptive Chemicals (EDCs) [START_REF] Crisp | Environmental endocrine disruption: an effects assessment and analysis[END_REF]. They have major impacts on human and wild life due to their influence on the networks of endocrine glands such as pituitary, thyroid, parathyroid, adrenals, and pancreas [START_REF] Sherwood | Human Physiology: From Cells to Systems[END_REF]. Human activity can sometimes introduce many endocrine compounds into the environment, which are capable of disturbing the endocrine system of aquatic, wildlife and sometimes humans. EDCs can also cause imbalance in immune system resulting in reproductive disorders. Moreover, certain types of cancer, birth defects and neurological disorders can also be induced by these contaminants. The foremost sources of EDCs are fish, meat, industrial waste water, dairy products, cosmetics and cleaning products, pesticides, herbicides and insecticides. These man-made compounds or natural products act on human body by fitting into hormone receptors, blocking the normal hormone or acting instead of the normal hormone in an irregular manner [START_REF] Johnson | Removal of endocrine-disrupting chemicals in activated sludge treatment works[END_REF][START_REF] Schlumpf | In vitro and in vivo estrogenicity of UV screens[END_REF].

Oxybenzone and atrazine are the major EDCs present in the environment. Oxybenzone is used as a broad-band UV filter in many concentrations ranging up to 10% in sunscreen products. It is usually used either alone or in combination with other UV filters .since it absorbs UVB (290-320 nm) and short-wave UVA (320-400 nm). It is also incorporated into environment by many other types of cosmetic products at concentrations ranging between 0.05 and 0.5% when used for photoprotection [START_REF] Burnett | Current sunscreen controversies: a critical review[END_REF]. When it is exposed to UV light, it was found to undergo oxidation and form an intermediate semiquinone which inactivates the anti-oxidant enzyme thioredoxin reductase, resulting in homeostasis of the epidermis [START_REF] Sundaram | The effect of UV radiation and sun blockers on free radical defence in human and guinea pig epidermis[END_REF]. On the other hand, atrazine is an EDC that alters the natural hormone system in animals [START_REF] Jiang | Occurance and removal of chloro-s-triazines in water treatment plants[END_REF]. It is widely used in agriculture as an herbicide. Although it is the most widely used herbicide in US [START_REF] Gianessi | Pesticide use in U.S. crop production[END_REF], its use remains controversial because of drinking water contamination. Atrazine also causes cancer, reproductive disorder, neurotoxicity, menstrual problems and birth defects. The half-life of the chemical was found to be in the range of 13 to 261 days in surface soil, and therefore, it can slowly migrate to underground water [START_REF] Frank | The economics of atrazine[END_REF].

Considering their toxicity, there is a lot of researches devoted to the development of new methodologies or processes for the elimination of hazardous organic moieties. Among them, membrane separation is getting thoughtful attraction from the scientific community, perhaps due to the simple synthetic procedure and high separation efficiency [START_REF] Ho | Membrane Handbook[END_REF][START_REF] Baker | Membrane Separation Systems: Recent Developments and Future Directions[END_REF][START_REF] Magalad | Preyssler type heteropolyacid-incorporated highly water-selective sodium alginate-based inorganic-organic hybrid membranes for pervaporation dehydration of ethanol[END_REF][START_REF] Magalad | Proton conducting properties of nanocomposite membranes of chitosan[END_REF]. PANi-HNT is a widely studied polymer for its electrical and optical properties, and it has been extensively used in many applications such as sensors, rechargeable batteries, anticorrosion coatings and selective membranes [START_REF] Arrascue | Gold sorption on chitosan derivative[END_REF][START_REF] Baghalha | Laeching of an oxide gold ore with chloride/hypochlorite solutions[END_REF]. Their inclusion in PSf membranes allows a modification of their properties leading to a significant improvement of performances such as water flux and separation of contaminants [START_REF] Murali | Polyaniline in situ modified halloysite nanotubes incorporated asymmetric mixed matrix membrane for gas separation[END_REF]. These membranes can be recycled many times, which is ecofriendly and cost effective [START_REF] Tuprakay | Lifetime and regeneration of immobilized titania for photocatalytic removal of aqueous hexavalent chromium[END_REF].

Present work involves the synthesis of various Wt.% PANi-HNT/PSf mixed matrix membranes (MMM) by phase inversion process. To the best of our knowledge, the rejection of oxybenzone and atrazine by PANi-HNT/PSf membranes was rarely investigated in the literature. Moreover, the manuscript provides a deep discussion about the parameters involved in the separation of emerging contaminants. XRD and ATR-IR spectroscopy were used to study the microstructure of the membranes and the morphology was also characterized by SEM images. Hydrophilicity of the various membranes was investigated by contact angle measurement and their performances in EDCs rejection were explored using different Wt%PANi-HNT/PSf membranes. Finally, the rejection measurements were also extended to the filtration of solutions containing EDCs mixed with water collected from a lake.

Experimental section

Materials

The various compounds used in this study are 2-Hydroxy-4methoxy-benzophenone (oxybenzone) (Merck), 2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine (atrazine) (Merck), polysulfone (Mw: 32,000 Da) (Sigma-Aldrich), 1-methyl-2-pyrrolidone (NMP) (Merck), HNT (Sigma-Aldrich), ammonium persulfate and HCl (Merck). All the chemicals used were of the reagent grade, and the solvents were dried and distilled before use as per the standard protocols.

Methods

Preparation of PANi-HNT

Polyaniline coated HNT was synthesized using in situ polymerization technique following the method reported by [START_REF] Zhang | Polyanillinecoated halloysite nanotubes via in-situ chemical polymerization[END_REF] with slight modifications [START_REF] Zhang | Polyanillinecoated halloysite nanotubes via in-situ chemical polymerization[END_REF]. 1.5 g of HNT was sonicated in 100 mL of distilled water to which 3 mL of aniline and 270 mL of 1 M HCl were added with continuous stirring at 10 °C. Temperature plays a critical role in this reaction as it avoids sudden dissociation of the acid. A solution of ammonium persulfate (7.5 g in 300 mL of 1 M HCl)was slowly added to the reaction mixture over a period of 90 min, and the mixture was then kept for 4 h at room temperature without any disturbance. Then, dark colored PANi modified HNT with chlorine salt was filtered and washed with distilled water. Finally the product was stirred with aqueous ammonium hydroxide to remove any excess of acid and to neutralize the product. In this process, chlorine is replaced by OH to form free base [START_REF] Stejskal | The formation of polyaniline and the nature of its structures[END_REF]. The obtained PANi-HNT was dried in oven at 70 °C for 48 h. Scheme 1 depicts the chemical reactivity of prepared PANi-HNT.

Preparation of PANi-HNT/ PSf composite membranes

The PANi-HNT/PSf composite membranes with desired weight percent of PANi-HNT were prepared by phase inversion process [START_REF] Padaki | Preparation and characterization of sulfonatedpolysulfone and N-phthloyl chitosan blend composite cation exchange membrane for desalination[END_REF]. The desired amount of PANi-HNT was taken into 16 mLof N-methylpyrollidine (NMP) of 99% purity, sonicated for 5 h at room temperature and stirred about30 min for uniform dispersion. Polysulfone (PSf) (MW = 32,000 Da) was discharged into the solution mixture and stirred for 24 h to obtain a viscous solution, which was casted on glass plate using glass rod and dipped in a coagulation bath to get composite membrane sheet of thickness ca.1 to 1.2 µm. The thickness of the membranes was kept uniform using side tape. Finally, membranes were washed several times with distilled water to remove excess of solvent that might be present during the preparation process and further by dipping into distilled water for 24 h [START_REF] Teow | Preparation and characterization of PVDF/TiO2 mixed matrix membrane via in situ colloidal precipitation method[END_REF]. The different ratios of polysulfone, PANi-HNT and NMP used for the preparation of the different membranes are showcased in Table 1.

Characterization of the PANi-HNT and mixed matrix membranes 2.3.1. ATR-IR and XRD

Attenuated total reflectance-infrared (ATR-IR) spectra were recorded on a Perkin Elmer FTIR 1650 Spectrophotometer in the range 400 and 4000 cm -1 . The PANi-HNT and mixed matrix membranes were compared by ATR-IR; the membranes were thoroughly dried for 24 h in a vacuum desiccator before recording the IR spectra. X-ray diffraction patterns of the PANi-HNT and mixed matrix membranes were recorded using a Shimadzu XRD-model 7000 with Cuka radiation scan rate of 1 °min -1 , and the crystalline nature of HNT on mixed matrix membranes was studied by XRD technique.

SEM analysis

SEM images of the membrane cross section were used to study the membrane morphology. The cross section was observed using a Scanning Electron Microscope (Technai 10 Phillips). The samples were prepared by freezing and fracturing the membranes with liquid nitrogen to provide smooth break of the membrane cross section. The prepared samples were affixed on carbon tape, making an aluminum disc stand and then sputter-coated with 15 nm of gold prior to SEM analysis.

Wettability study of membranes

The water uptake and contact angle are the two main two parameters to determine the hydrophilicity of the membranes [START_REF] Jyothi | Khantong Soontarapa, Eco-friendly membrane process and product development for complete elimination of chromium toxicity in wastewater[END_REF]. The water uptake of the prepared membrane was performed by recording the change in the weight of the dry membrane and the membrane which was soaked in distilled water. Membranes were thoroughly rinsed with distilled water, and then dried in vacuum desiccators with a vacuum of 120 mm Hg for 24 h. The dried membrane was sliced into small pieces of ca.1 cm 2 area and was further immersed in distilled water. Eventually, these pieces of membranes were taken out after 24 h interval of time, and excess water on the surface was gently removed by a blotter. Finally, the weight of the soaked membranes was quickly measured. And the water uptake percentage was calculated using the Eq. [START_REF] Crisp | Environmental endocrine disruption: an effects assessment and analysis[END_REF].

⎜ ⎟ = ⎛ ⎝ -⎞ ⎠ × %water uptake W W W 100 w d d (1)
where, W w and W d are the weights of wet and dried membranes, respectively.

The contact angle between the water and the membrane was measured by using a FTA-200 Dynamic contact angle measurement by sessile drop method. A water droplet was placed on the membrane at five different places and the average value is reported.

Ion exchange capacity (IEC)

The determination of the ion exchange capacity consists in measuring the number of counter ions exchangeable in membrane in milliequivalents of H + per weight of the dry membrane. The ion exchange capacity gives information about the density of ionizable functional groups present on the membrane. These ionizable groups are majorly responsible for the charged nature of the membrane and for membrane conductivity [START_REF] Vignesh Nayak | Khantong Soontarapa Zwitterionic, Ultrafiltration membranes for As (V) rejection[END_REF]. The membranes were cut into 1cm 2 square shaped pieces, dried well and weight was noted. Then, they were kept in a 10 mL of 1 M HCl solution for 24 h for exchange of cations and rinsed with distilled water. Finally, the membranes were kept in 10 mL of 1 M NaCl solution for 24 h. The ion exchange capacity was calculated by obtained HCl solution through titration method as described in the literature [START_REF] Klayson | Preparation characterization of sulfonatedpolyethersulfone for cation-exchange membranes[END_REF]. The IEC was calculated using Eq. ( 2)

⎜ ⎟ = ⎛ ⎝ ⎞ ⎠ × IEC AB m 100 dry (2)
where, A = Concentration of the NaOH used, B = Volume of NaOHsolutionconsumed during the titration and M dry = weight of the dry membrane.

Membrane performance study 2.4.1. Rejection of oxybenzone and atrazine

The rejections of EDCs were carried out by using self-constructed dead-end filtration unit. The experimental filtration procedure proposed by Jyothi et al. [START_REF] Jyothi | The effect of UV irradiation of PSf/TiO2 mixed matrix membrane for chromium rejection[END_REF] was followed for rejection measurements. Prepared membranes of desired Wt.% were cut into round shape of 5 cm 2 and fixed at the bottom of the unit. The unit is then filled with 500 mL of solution containing 3 ppm of afore-mentioned EDCs. The filtration experiments were run separately for oxybenzone and atrazine. For each applied pressure, 20 mL of the solution was collected for determination of EDCs concentration. Solution were analyzed at λ max = 235 nm for oxybenzone and at λ max = 225 nm for atrazine, using a UV-Visible Spectrophotometer. The applied pressure was varied from 200 to 1000 kPa with the difference of 200 kPa. For each pressure, a new membrane and feed solution was used. The percentage of rejection was calculated using Eq. ( 3).

⎜ ⎟ = ⎛ ⎝ -⎞ ⎠ × C % rejection 1 C 100 p f (3)
where, C p and C f are the permeate and feed concentrations, respectively.

The above procedure was repeated by using each of the following membranes one by one, viz, 0.25 wt% of PANi-HNT/PSf membrane, 0.5 wt% of PANi-HNT/PSf membrane, 0.75 wt% of PANi-HNT/PSf membrane, and 1 wt% of PANi-HNT/PSf membrane for oxybenzone and atrazine.

Pore size identification

In this paper, the mean pore radius r p of the various membranes was estimated by fitting rejection of atrazine and oxybenzone with a hydrodynamic model that considers only steric exclusion. This approach has been implemented many times in literature and the value of pore radius is usually adjusted in Eq. ( 4) to fit a complete rejection curve of a neutral solute such as glucose [START_REF] Escoda | Assessment of dielectric contribution in the modeling of multi-ionic transport through nanofiltration membranes[END_REF].

= -

---

φK φK Pe %rejection 1 1 [(1 )(exp( ))] c c ( 4
)
Pe denotes the Peclet permeation number defined as: (5)

= ∞ Pe K P K D η r Δ 8
= - + - - K φ λ λ λ (2 )(1 0.054 0.988 0.441 ) c 2 3
(5.1)

⎜ ⎟ = ⎛ ⎝ -⎞ ⎠ φ r r 1 s p 2 (5.2)
In this study, the pore radius has been identified for each pressure separately to discuss the exclusive phenomena governing rejection of atrazine and oxybenzone.

Rejection of oxybenzone and atrazine from a lake water

The rejections of EDCs were also carried out by using feed solution collected from a nearby lake. The filtration procedure is identical to that previously described. The feed solution was prepared in the laboratory by mixing the compounds in the lake water. Water was collected from the lake and stored in laboratory for 24 h without disturbing. Afterwards, upper layer of water was collected and used for the preparation of feed sample. The rejection of EDCs was studied in the same conditions that those used for synthetic solutions.

Results and discussion

Characterization of the membranes

Fig. 1 depicts the XRD spectra of HNT, PANi-HNT and composite membranes. The XRD spectra of HNT with a set of characteristic peaks at 13 °, 21 °and 27 °of two theta, whereas XRD spectrum of PANi-HNT, which shows peaks with decreased intensity compared with HNT spectra. Further, the peaks were shifted towards left to 12 °, 20 °and 27 °of two theta, which may be attributed to the fact that the interaction of in situ generated polymer chain into the intra molecular layer of HNT [START_REF] Padaki | The effect of UV irradiation on PSf/TiO2 mixed matrix membrane for chromium rejection[END_REF]. The XRD spectrum of HNT and PANi-HNT matches with literature standard, which clearly shows the successful modification of HNT to PANi-HNT [START_REF] Zhang | Polyanillinecoated halloysite nanotubes via in-situ chemical polymerization[END_REF].The XRD spectrum of PSf membrane showing broad peaks at 18 °of two theta, while the XRD spectra of 1% PANi-HNT/PSf membrane. In the case of these mixed matrix membranes, PANi-HNT peaks were not observed due to low concentration in the polymer matrix. 2a exhibits a band characteristic for NH stretching vibrations of PANi-HNT in the range 3600-3683 cm -1 . Two distinguishing bands observed at 1588 and 1498 cm -1 were attributed to C]C stretching of benzenoid ring and C]N stretching vibrations of quinoid rings, respectively. Further, the peaks at 1312 cm -1 correspond to CeN stretching These all peaks are not in HNT spectrum, which confirms the formation of PANI HNT. Fig. 2b represents the IR spectra of membranes which shows bending vibrations of CeH stretch at 1156 cm -1 and stretching vibrations in the range 1105 cm -1 due to O]S]O of the PSF. IR spectra of 0.5 wt% PANi-HNT/PSf and 1 wt% PANi-HNT/PSf membranes. The presence of PANi-HNT on PSf membrane could modify the resulting bands either by change in the relative intensity or by shifting some of the bands this is evident as C]N and C]C bands have been shifted from 1589 and 1508 cm -1 to 1585 and 1503 cm -1 .This observation can be attributed to the intra molecular bonding between PANi-HNT and PSf.

Figure 3 illustrates the cross-section images of PSf and mixed matrix membranes. EDAX study of the membrane was performed and it shows presence of Al, Si, and N in the membrane, which confirms the presence of PANi-HNT in the membrane matrix. Of particular interest, Fig. 3(b-d) exhibit mixed matrix membranes with the concentration of PANi-HNT increases from 0.25 to 1%, respectively. The cross-section reveals the presence of a dense layer, a finger like middle layer and a bottom supportive layer system. The finger like projections are narrow in PSf membrane, whereas increasing the concentration of PANi-HNT, this projection became broad while micro channels on the surface layer become thinner. In the bottom layer, however, the macrovoids are longer with the increase in the concentration of the PANi-HNT, below the finger like projections. There are two important parameters, effectively for such macrovoids during phase inversion are; i) hydrophilicity and ii) viscosity of the casting solution [START_REF] Padaki | The effect of UV irradiation on PSf/TiO2 mixed matrix membrane for chromium rejection[END_REF]. Hydrophilicity increases the intake of non-solvents (water) in the solvent/non-solvents exchange and higher the viscosity decreases the rate of solvent exchange which causes macrovoids [START_REF] Nayak | Novel modified poly vinyl chloride blend membranes for removal of heavy metals from mixed ion feed sample[END_REF]. In this case the PANi-HNT is hydrophilic in nature meanwhile metal oxides decrease the viscosity of the casting solution. Hence, macrovoids are larger with respect to higher concentration of PANi-HNT The hydrophilicity of the membranes also supports the above phenomenon, and was confirmed by water uptake and contact angle studies. In higher concentration, the water movement is irregular because of more hydrophilic interaction doing phase inversion. Hence, twisted finger like projections were observed in higher concentration of PANi-HNT membranes.

Fig. 4 demonstrates the water uptake and contact angle for the prepared membranes of various concentrations of PANi-HNT distributed over PSf membranes. The contact angle decreases from 74 ± 2 to 55 ± 4 °as the concentration of the PANi-HNT increases from 0 to 1%. The water uptake was increased from 29.9 to 31.43%, after the incorporation of 0.25% PANi-HNT over PSf membrane. Similarly, the water uptake increases substantially upon the increase in the concentration of PANi-HNT, that is, 0.5% PANi-HNT, 0.75% PANi-HNT, and 1% PANi-HNT to 46.67%, 85.15% and 90.18%, respectively. The presence of PANi-HNT on the surface of the PSf membrane drastically improves the water uptake/hydrophilicity. Hydroxyl polarity is able to interact with water molecules through Van der Waal's force and hydrogen bonding, which helps to enhance the hydrophilicity of the membrane [START_REF] Sakai | Photoinduced hydrophilic conversion properties of titaniananosheets[END_REF]. In PANi-HNT, the presence of NH 2 groups leads to the increase of Van der Waal's interactions with water molecules. However, it is also possible that the higher affinity of PANi-HNT for water compared to polysulfone, which increases the penetration velocity of water into mixed matrix membrane during the phase inversion process [START_REF] Padaki | The effect of UV irradiation on PSf/TiO2 mixed matrix membrane for chromium rejection[END_REF]. Hence, mixed matrix membranes are hydrophilic in nature as compare to plane PSf membrane.

PANi-HNT is a charged molecule, and therefore, IEC of the membrane (given in Table 2) is also an important characteristic to enhance the membrane performance. The amine group from PANi has capability to do the ion exchange. Meanwhile, prepared PANi-HNT is in basic form with OH -counter ions. Hence, prepared membranes exhibit the charge interaction phenomenon in performance. Table 2 shows the ion exchange capacity of the membranes. IEC capacity depends on the concentration of PANi-HNT; higher concentration of the PANi-HNT enhances the exchange capacity.

Water flux study

Pure water flux study is one of the important parameters in judging the porous nature of the membrane [START_REF] Fengmei | Preparation and characterization of PVDF/TiO 2 hybrid membranes with different dosages nano TiO 2[END_REF]. Fig. 5 shows the pure water flux at different operating pressures for various PANi-HNT/PSf membranes. In the case of the plain PSf membrane and 0.25% PANi-HNT/ PSf membrane, no water flux is observed even at 800 kPa of pressure indicating the hydrophobic nature of these membranes, which hinders the penetration of water. However, a high water flux was observed in the cases of higher percentage of PANi-HNT membranes with the increase in pressure. These results are almost linear with respect to transmembrane pressure. It was observed that there is a constant increase in the water flux with respect to increase in the weight percentage of PANi-HNT. This is perhaps due to the hydrophilic nature of the substance. Hydrophilicity increases with respect to concentration of PANi-HNT and the same trend was followed in the water flux study. The highest concentration i.e. 1% PANi-HNT evinced maximum water flux of 40.15 L/m 2 h at 200 kPa and 86.5 L/m 2 h at 800 kPa pressure. The water flux was found to decrease with the decrease in concentration of PANi-HNT irrespective of the pressure considered. Hence, the productivity of the membrane depends basically on its hydrophilic character.

Rejection study using PANi-HNT/PSfmembrane

The rejection of trace organic molecules by membranes depends on the feed parameters like molecular weight (MW), molecular size (length and width), acid dissociation constant (pKa), hydrophobicity/hydrophilicity (log K ow ), diffusion constant (Dp) and membrane properties such as pore size (MWCO value), surface charge (IEX), or hydrophobicity/hydrophilicity (measured as contact angle and water uptake) and surface morphology [START_REF] Yoon | Removal of endocrine disrupting compounds and pharmaceuticals by nanofiltration and ultrafiltration membranes[END_REF]. PANi-HNT is more hydrophilic and negatively charged. Hence, PSf /PANi-HNT composite membrane were used for the removal of atrazine and oxybenzone. Fig.

shows the extent of rejection of oxybenzone with respect various applied pressures. The results reveal that the membrane of lower concentration of 0.25% PANi-HNT/PSf evinced a rejection of ca. 55% of these organics. The 0.5, 0.75 and 1% PANi-HNT/PSf membranes evidenced 95% of rejection as higher hydrophilicity enhances the rejection. The membrane performance was studied for various pressures, from 200 to 800 kPa, however, the same rejection values were found for all the applied pressures.

The extent of rejection of atrazine with respect to applied pressure is showcased in Fig. 7. The 1% PANi-HNT/PSf membrane displayed maximum rejection of ca.50% was observed at a lower pressure of 200 kPa while the same performance was continuously found even at higher pressure. In the cases of 0.25, 0.5 and 0.75% PANi-HNT/PSf membranes, a rejection of around 30% was observed at lower pressure of 200 kPa. The rejection increases with increase in Log K ow for both the compounds (oxybenzone and atrazine having Log K ow > 2.5), which indicates hydrophobic nature. Therefore, higher rejection was observed with highly hydrophilic membranes [START_REF] Bellona | Factors affecting the rejection of organic solutes during NF/RO treatment -a literature review[END_REF]. In the instance of atrazine (Fig. 7), the 1% PANi-HNT/PSf membrane exhibitedca.50% of rejection; however, decreasing concentration of PANi-HNT of 0.25, 0.5 and 0.75% illustrated30% of the atrazine rejection. Furthermore, the rate of the rejection decreases with an increase in the applied pressure. The anomalous behavior of the separation results was justified using properties of organic compounds.

Almost 95% and 35% of rejections were observed in the cases of oxybenzone and atrazine, respectively, using the same membranes. The transport mechanism in the membrane separation was affected by the properties of feed solution [START_REF] Yoon | Removal of endocrine disrupting compounds and pharmaceuticals by nanofiltration and ultrafiltration membranes[END_REF]. Table 3shows the physical properties of the oxybenzone and atrazine. The pKa value of atrazine is 1.7 which is acidic in nature, and it can undergo chemical reaction with the membranes as it is a chloro substituted derivative. Hence, it can damage the surface of the membrane by increasing the pore size. Surface which cause enlarging the pore size, meanwhile, diffusion coefficient of the atrazine is higher than the oxybenzone. Moreover, oxybenzone is considerably larger in size than atrazine and the steric exclusion is also strongly lower with the latter. Hence, the rejection of atrazine is drastically lower than that of oxybenzone.

Each PANI-HNT/Psf membranes shows significantly higher rejection than the Psf membranes and it is especially true when the percentage of PANI-HNT is high (1%). Moreover, rejection obtained with the 1% PANI-HNT/Psf is found to be notably higher than those reported in literature for UF membranes (0-10% for atrazine [START_REF] Devitt | Effects of natural organic matter and the raw water matrix on the rejection of atrazine by pressure-driven membranes[END_REF][START_REF] Yoon | Nanofiltration and ultrafiltration of endocrine disrupting compounds, pharmaceuticals and personal care products[END_REF] and 70% for oxybenzone [START_REF] Yoon | Nanofiltration and ultrafiltration of endocrine disrupting compounds, pharmaceuticals and personal care products[END_REF]) and even for loose NF membranes (30-40% for atrazine [START_REF] Devitt | Effects of natural organic matter and the raw water matrix on the rejection of atrazine by pressure-driven membranes[END_REF][START_REF] Yoon | Nanofiltration and ultrafiltration of endocrine disrupting compounds, pharmaceuticals and personal care products[END_REF] and 70%-80% for oxybenzone [START_REF] Comerton | The rejection of endocrine disrupting and pharmaceutically active compounds by NF and RO membranes as a function of compound and water matrix properties[END_REF]). For this reason, EDCs are usually removed from contaminated solution by hybrid membrane processes, such as membrane bioreactor and/or membrane distillation [START_REF] Wijekoon | Rejection and fate of trace organic compounds (TrOCs) during membrane distillation[END_REF]. However, considering the performances highlighted in this study, PANI-HNT-Psf membranes seems to be a relevant alternative to hybrid processes due to their modified properties.

The rejections obtained for the various filtration experiments were used to estimate the mean pore radius of the two membranes by considering only steric exclusion (Eqs. 45.2). It is expected that only one pore radius can describe a whole curve of rejection, but the rejection decrease when pressure increases does not allow the determination of a unique pore size. Thus, pore radius was estimated for three pressures corresponding to 200, 500 and 800 kPa, as it is depicted in Fig. 8 in the case of atrazine rejection by the 1% PANI-HNT/PSf membrane. The values estimated for all conditions are summarized in Table 4 for discussion.

As it can be seen in Table 4, the identified pore radius increases with pressure, which is obvious since experimental rejection was found to decrease when pressure increases. Nonetheless, identified values are close to 2-4 nm, meaning that membranes can be classified as ultrafiltration membranes with low molecular weight cut-off. The decrease of rejection for increasing pressure being probably induced by a strong concentration polarization, it is probably more coherent to discuss the pore radii obtained at 200 kPa. From values identified with the two solutes, it can be seen that membrane exhibits different pore sizes, which is physically incoherent. This observation tends to show that rejection is probably governed by other phenomena than steric exclusion, such as electrostatic interactions. Consequently, a hydrodynamic model is not sufficient to describe rejection of such molecules and a more complex model including electric and dielectric exclusions [START_REF] Déon | How to use a multi-ionic transport model to fully predict rejection of mineral salts by nanofiltration membranes[END_REF] should be developed for this purpose.

Rejection study for the laboratory prepared feed sample

Further, the membrane was used to remove EDCs from laboratory feed samples prepared with a real water. Indeed, it was previously reported that rejection of EDCs can also vary depending on feed water chemistry [START_REF] Kim | Removal of contaminants of emerging concern by membranes in water and wastewater: a review[END_REF].

The solution containing 3 ppm of oxybenzone and atrazine were prepared using waste water collected from a lake and the rejection was investigated using the same filtration unit and procedure. Fig. 9 shows the evolution of oxybenzone rejection with respect to various applied pressure. In case of 0.25% PANi-HNT/PSf membrane, there was no rejection, but in the cases of 0.5, 0.75 and 1% PANi-HNT/PSf an average rejection of 20% was observed. Almost the same average rejection was observed for atrazine in Fig. 10with the same mixed matrix membranes. With these samples, the rejection of atrazine and oxybenzone appears no longer to be governed by steric exclusion, leading to similar rejections although their sizes are significantly different.

With an increase in the feed water concentration, a concentration differential is created across the membrane and resulting in ahigh diffusion potential. Diffusion across the membrane is one of the main driving forces for the maximum permeation of organic compounds [START_REF] Yoon | Removal of endocrine disrupting compounds and pharmaceuticals by nanofiltration and ultrafiltration membranes[END_REF]. The recovery of the HAAS, DBP and total THM by NF membranes was studied by Chellam and Taylor, who found that the rejection decreases by increasing recovery [START_REF] Chellum | Simplified analysis of contaminant rejection during ground and surface water nanofiltration under the information collection rule[END_REF]. Hence, in this case, the feed solution was full of other organic compounds. It increases the diffusion potential across the membrane which causes the falls in rejection.

Conclusion

The prepared membranes have the ability to remove the hydrophobic organic trace compounds. The polysulfone is a hydrophobic membrane and it was converted hydrophilic by mixing with PANI-HNT. The hydrophilic and negatively charged membrane showed better performances with both charge and hydrophilicity, which are the main properties governing transport through the membrane. The water uptake and flux were higher for higher concentrations of PANi-HNT. The physical properties of the feed solution play a crucial role in the rejection studies. For instance, the log K ow and the size of the molecule have shown a great influence on the separation performances. The compounds which are having log K ow > 2.8 can be removed using these membranes. Oxybenzone showed 95% and atrizines ca.50% of rejection by the reported membranes.

Scheme 1 .

 1 Scheme 1. Preparation of PANi-HNT by chemical reaction

  and K d the hindrance factors for convection and diffusion, respectively and ϕ the steric partition coefficient defined by[START_REF] Lang | Comparison on gas separation between PES (polyethersulfone)/MMT (Na-montmorillonite) and PES/TiO 2 mixed matrix membranes[END_REF]:

Fig. 1 .

 1 Fig. 1. XRD spectra of HNT, PANI-HNT, PSf membrane and 0.5% PANi-HNT/PSf membrane.

Fig. 2 .

 2 Fig. 2. ATR-IR spectra of (a) HNT and PANi-HNT (b) membranes of plane PSF and PANi-HNT/PSf.

Fig. 3 .

 3 Fig. 3. SEM monographs of (a) Plain PSf cross section (b) 0.25% PANi-HNT/PSf cross section (c) 0.5% PANi-HNT/PSf cross section (d) 1% PANi-HNT/PSf cross section (e) EDAX analysis of the membrane

Fig. 2

 2 Fig.2portrays the stacked ATR-IR spectra of PSf membrane, PANi-HNT and mixed matrix membranes. Of particular interest, Fig.2aexhibits a band characteristic for NH stretching vibrations of PANi-HNT in the range 3600-3683 cm -1 . Two distinguishing bands observed at 1588 and 1498 cm -1 were attributed to C]C stretching of benzenoid ring and C]N stretching vibrations of quinoid rings, respectively. Further, the peaks at 1312 cm -1 correspond to CeN stretching These all peaks are not in HNT spectrum, which confirms the formation of PANI HNT. Fig.2brepresents the IR spectra of membranes which shows bending vibrations of CeH stretch at 1156 cm -1 and stretching vibrations in the range 1105 cm -1 due to O]S]O of the PSF. IR spectra of 0.5 wt% PANi-HNT/PSf and 1 wt% PANi-HNT/PSf membranes. The presence of PANi-HNT on PSf membrane could modify the resulting bands either by change in the relative intensity or by shifting some of the bands this is evident as C]N and C]C bands have been shifted from 1589 and 1508 cm -1 to 1585 and 1503 cm -1 .This observation can be attributed to the intra molecular bonding between PANi-HNT and PSf.Figure3illustrates the cross-section images of PSf and mixed matrix membranes. EDAX study of the membrane was performed and it shows presence of Al, Si, and N in the membrane, which confirms the presence of PANi-HNT in the membrane matrix. Of particular interest, Fig.3(b-d) exhibit mixed matrix membranes with the concentration of PANi-HNT increases from 0.25 to 1%, respectively. The cross-section reveals the presence of a dense layer, a finger like middle layer and a bottom supportive layer system. The finger like projections are narrow in PSf membrane, whereas increasing the concentration of PANi-HNT, this projection became broad while micro channels on the surface layer become thinner. In the bottom layer, however, the macrovoids are longer with the increase in the concentration of the PANi-HNT, below the finger like projections. There are two important parameters, effectively for such macrovoids during phase inversion are; i) hydrophilicity and ii) viscosity of the casting solution[START_REF] Padaki | The effect of UV irradiation on PSf/TiO2 mixed matrix membrane for chromium rejection[END_REF]. Hydrophilicity increases the intake of non-solvents (water) in the solvent/non-solvents exchange and higher the viscosity decreases the rate of solvent exchange which causes macrovoids[START_REF] Nayak | Novel modified poly vinyl chloride blend membranes for removal of heavy metals from mixed ion feed sample[END_REF]. In this case the PANi-HNT is hydrophilic in nature meanwhile metal oxides decrease the viscosity of the casting solution. Hence, macrovoids are larger with respect to higher concentration of PANi-HNT The hydrophilicity of the membranes also supports the above phenomenon, and was confirmed by water uptake and contact angle studies. In higher concentration, the water movement is irregular because of more hydrophilic interaction doing phase inversion. Hence, twisted finger like projections were observed in higher concentration of PANi-HNT membranes.

Fig. 4 .

 4 Fig. 4. Water uptake and contact angle of the membranes

Fig. 5 .Fig. 6 .

 56 Fig. 5. Pure water flux of the membranes

Fig. 7 .

 7 Fig. 7. Rejection of Atrazine at different pressure

Fig. 8 .

 8 Fig. 8. Estimation of the mean pore radius from experimental rejection of atrazine by the 1% PANI-HNT/PSf membrane

Fig. 9 .

 9 Fig. 9. Rejection of laboratory prepared field water with oxybenzone at different pressure.

Table 1

 1 Different concentration of PSf and PANi-HNT in the various membranes.

	Membrane type	Polysulfone	PANi-HNT	NMP
	0.25% PANi-HNT/PSf membrane	18.75%	0.25%	81%
	0.5% PANi-HNT/PSf membrane	18.5%	0.5%	81%
	0.75% PANi-HNT/PSf membrane	18.25%	0.75%	81%
	1% PANi-HNT/PSf membrane	18%	1%	81%

Table 2

 2 Ion exchange capacity of the mixed membrane.

Membrane type IEC (m equ/g) 0.25% PANi-HNT/PSf membrane 13.88 0.5% PANi-HNT/PSf membrane 15 0.75% PANi-HNT/PSf membrane 15.6 1% PANi-HNT/PSf membrane 15.6

Table 3

 3 Physical properties of atrazine and oxybenzone.

	Properties	Atrazine			Oxybenzone
	Molecular Weight	215.68 Da			228.24 Da
	Molecular size	0.788 nm			3.16 nm
	(length and
	width)
	Acid dissociation	1.7			7.6
	constant (pKa)
	Log K ow	2.7			3.79
	Diffusion
	coefficient
	Structure		Cl			OH	O
		N	N	CH 3
		H 3 C	N	C H 3	H 3 C	O

5.4 × 10 -10 m 2 /s 3 × 10 -8 cm 2 /s

Table 4

 4 Pore radius of the various membranes estimated for 3 pressures from atrazine and oxybenzone rejection.

	Membranes	0.25%			0.5%			0.75%			1%
		PANi-HNT/PSf		PANi-HNT/PSf		PANi-HNT/PSf		PANi-HNT/PSf
	Pressures (kPa)	200	500	800	200	500	800	200	500	800	200	500	800
	r p (nm) from atrazine	2.35	4.5	8	2.2	3.9	8	2.3	3.9	8	1.7	2.1	2.3
	r p (nm) from oxybenzone	2.5	2.6	2.65	3.3	3.9	3.9	3.6	3.8	3.9	3.7	3.8	3.8

Acknowledgements

The authors sincerely acknowledge the Indian Ministry of Drinking water and Sanitation (no. 11017/33/2012-WQ), Department ofscience and technology for SERB (SB/FT/CS-045/2014), Nano mission projects (SR/NM/NS-20/2014) and the Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST MISiS» (№ К3-2016-032) for financial support.


Ankit Shah

Anurag Kumar

Alexander G Hauptmann

IEEE Bhiksha Raj Fellow

A Closer Look at Weak Label Learning for Audio Events

Keywords: Audio Events, Weak Labels, Weakly Supervised, CNN

Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED. However, a deeper understanding of how weak labels affect the learning for sound events is still missing from literature. In this work, we first describe a CNN based approach for weakly supervised training of audio events. The approach follows some basic design principle desirable in a learning method relying on weakly labeled audio. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events. We show how these aspects of weak labels affect the generalization of models. More specifically, we study how characteristics such as label density and corruption of labels affects weakly supervised training for audio events. We also study the feasibility of directly obtaining weak labeled data from the web without any manual label and compare it with a dataset which has been manually labeled. The analysis and understanding of these factors should be taken into picture in the development of future weak label learning methods. Audioset, a large scale weakly labeled dataset for sound events is used in our experiments.

I. INTRODUCTION

M ULTIMEDIA data on the internet is growing at an exponential rate and according to one estimate 1 , around three-quarter of internet traffic currently consists of videos. It is expected to grow more in coming years. This clearly demands methods which can automatically analyze the contents of these multimedia data. Automatically analyzing multimedia data is necessary for their successful indexing, search and retrieval as required by the end application. Audio Event Detection (AED) or Sound Event Detection deals with the problem of automatically analyzing the audio content of these multimedia data with respect to different sound events. Besides being important for content based search and retrieval of videos on the web, AED plays a vital role in other areas as well. Sounds are crucial to our understanding of the environment around us, and the next generation of artificial intelligence systems requires capabilities to recognize, understand and interpret sounds just as humans can. Hence, AED is essential for improving human-computer interaction as well. Voice-based smart assistants such as Google Home, Amazon Alexa, Apple Siri, etc. can draw context-related information using AED 2 and can improve their interaction with users by using such information. Another important application is surveillance. The importance of audio based surveillance has been known and studied for a long time [START_REF] Clavel | Events detection for an audiobased surveillance system[END_REF]. Audio does not require "line of sight" and are relatively easier to capture and transmit for surveillance purposes.

The development of large-scale audio event detection systems has been hindered for a long time by the availability of a large labeled dataset. The problem primarily lies in annotating audio recordings for sound events. Annotating audio recordings with time stamps of occurrences of sound events is a difficult, time consuming and an expensive task. This is evident in the small scale of several sound event datasets and challenges released in the last decade or so [START_REF] Stowell | Detection and classification of acoustic scenes and events[END_REF], [START_REF] Mesaros | Tut database for acoustic scene classification and sound event detection[END_REF], [START_REF] Zieger | Acoustic event detection-itc-irst aed database[END_REF], [START_REF] Piczak | Esc: Dataset for environmental sound classification[END_REF], [START_REF] Salamon | A dataset and taxonomy for urban sound research[END_REF]. The scale here refers to both (1) amount of available training data for each event and (2) vocabulary of sound events. Labeled audio recordings with time stamps are often referred to as strongly labeled data [START_REF] Kumar | Audio event detection using weakly labeled data[END_REF]. Hence, supervised learning methods which rely on event exemplars for training, or in other words time stamped information to extract out event exemplars, is not expected to be a scalable approach to AED, as large amounts of strongly labeled data is very hard to come by.

Audio event detection using weakly labeled data, proposed in [START_REF] Kumar | Audio event detection using weakly labeled data[END_REF], addresses the problems of strongly labeled data. In weakly labeled audio data, only the presence of absence of an event is known. That is an audio recording is just tagged with respect to an event. For example, for an audio recording which might be several minutes long, we just know whether a sound event, say dog barking occurred or not. No other information such as time stamps or count of occurrences of dog barking within the recording is known. Hence for weak labels, manually tagging audio recordings with weak labels is much easier compared to annotating them with timestamps of events. Moreover, as highlighted in [START_REF]Deep cnn framework for audio event recognition using weakly labeled web data[END_REF], weak labels also address ambiguities or interpretation issues (faced by annotators), in defining the boundaries of sound events. It also opens up the possibility of training AED systems from the vast amount of audio (or video) data available on the web. Metadata associated with web audios (or videos) can be used to obtain weak labels for audio recordings, which can further reduce labeling effort.

Overall, learning audio events using weakly labeled data is a promising paradigm to scale audio event detection. Several methods have been proposed for weakly supervised training of sound events. AED with weak labels is now a task in the annual IEEE AASP sound event and scene classification challenge as well (DCASE 2017 [START_REF] Mesaros | Dcase 2017 challenge setup: Tasks, datasets and baseline system[END_REF], DCASE 2018 3 ). Audioset [START_REF] Gemmeke | Audio set: An ontology and human-labeled dataset for audio events[END_REF], a large scale weakly labeled audio event dataset have further helped accelerate research in this direction.

However, learning sound events from weakly labeled data comes with its own set of problems and challenges. Although, several methods (see related work section) have been proposed for AED using weak labels in the last couple of years, a deeper understanding and analysis of the problems and challenges arising in large-scale AED using weak labels is entirely missing. In this paper, we take a closer look at AED using weakly labeled data. We first describe a Convolutional Neural Network (CNN) based approach for large-scale audio event detection using weak labels. We then propose different ways in which "label noise" naturally manifests itself into weak label paradigm for sound events. These characteristics of weak label learning are then analyzed to understand how they affect the generalization capabilities of the trained model. This analysis and understanding is not only desirable but necessary for further development of audio event detection using weakly labeled data. One needs to factor in these characteristics when developing a learning algorithm.

The primary motivation behind weak label learning is that it shows a way to scale the learning process by easing the data acquisition and labeling process. Primarily its significance lies in the fact that it is a learning paradigm where data from the web can be exploited. However, it comes at the cost of noise in the training data. This aspect of weakly supervised learning has been explored in the field of computer vision, [START_REF] Lu | Learning from weak and noisy labels for semantic segmentation[END_REF][12] [START_REF] Feng | Image tag completion by noisy matrix recovery[END_REF] to cite a few.

However for sounds, weakly supervised learning has only recently come up and the impact and relevance of factors such as noise in the data is yet to be analyzed, understood and explored. This paper is an attempt in that direction. For sounds, weakly labeled data from the web (such as YouTube) contains noise primarily in two forms, Signal noise and Label noise. We use the term signal noise to refer to a variety of signal degrading factors. Often, consumer-generated web audios or videos are recorded under unstructured conditions. Occurrences of sound events of interests are often corrupted by either noise and/or events of which are not of interest. The recording conditions, environments, styles, instruments can all lead to huge intra-class variations. This does make the problem much more challenging. The focus of this work, however, is on analyzing the other type noise in weakly supervised learning of sounds, namely, the Label noise.

Label noise in weakly labeled audio occurs naturally in different ways. In this work, we consider two important factors. The first one is the Label Density. Label density signifies the portion of the recording (out of the total length) during which the tagged sound event is actually present in the recording. Consider an example. We have an audio recording (R 1 ) of 10 seconds containing only a total of 1 second of the tagged event in it and another audio recording (R 2 ) of the same duration in which the tagged event is present for 3 http://dcase.community/challenge2018/ a total of 5 seconds. Clearly, the density of labeled event in R 2 is higher. In other words, R 1 in its entirety, is a weaker representation of the event compared to R 2 . We can define label density noise as a measure of weakness of a given audio recording with respect to the tagged audio event.

The second form of label noise comes from the actual wrong labeling of the audio recordings. Sound events can often be hard to interpret, and this might lead to wrong labels, even when the labeling is done manually. This becomes a bigger problem when we work on large scale, with a large number of audio events. For example, Audioset which have been manually labeled; the quality of the labels has been roughly estimated to be less than 90%4 for several events. The problem can grow by several folds when we try to mine the data directly from the web and obtain weak labels by exploiting the associated metadata. In this case, a considerable portion of the weak labels is expected to be wrong. We refer to this form of label noise as label corruption noise.

Clearly, label density noise is an inherent characteristic of weak label learning. Corrupt weak labels can also easily occur and hence often the two forms of noises are simultaneously present. This is especially true for weakly labeled data obtained from the web in which no manual labeling effort was employed. We try to understand these factors by empirically analyzing the performance of a state-of-art CNN based method for weakly labeled audio event classification. We first describe our CNN based approach for weakly supervised training of sound events in Section III. We then describe the different label noises in details and the corresponding experimental design to analyze them in Section IV. We discuss our experiments and results in Section V and then conclude in Section VI. An overview of related works is given in the next section.

II. RELATED WORK

Some of the earliest works on automated content analysis of audio recordings were done for content based retrieval of audio recordings [START_REF] Wold | Content-based classification, search, and retrieval of audio[END_REF] [START_REF] Guo | Content-based audio classification and retrieval by support vector machines[END_REF]. Other earlier works focused on the detection of specific audio events such as gunshots and screams for surveillance purposes [START_REF] Valenzise | Scream and gunshot detection and localization for audio-surveillance systems[END_REF] [START_REF] Gerosa | Scream and gunshot detection in noisy environments[END_REF]. Analyzing different type of acoustic features and time-frequency representations with different types of classifiers is central to these works. More recently, Audio event detection research has started to receive much more attention, primarily due to its application in a wide range of systems; beyond the obvious content-based retrieval of multimedia data and surveillance. Human-computer interaction [START_REF] Maxime | Sound representation and classification benchmark for domestic robots[END_REF], smart devices including voice-based assistants 5 , smart homes and health monitoring systems [19][20][21], [START_REF] Li | Acoustic fall detection using a circular microphone array[END_REF], wildlife monitoring [START_REF] Stowell | Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning[END_REF], all require automated audio content analysis in terms of sound events.

Several fully supervised learning methods have been proposed in the last few years for audio event detection. For example, [START_REF] Zhuang | Realworld acoustic event detection[END_REF] used a GMM-HMM architecture similar to speech recognition for AED. Bag of audio words representation built over different features is a popular and widely used representation, often combined with different classifiers [START_REF] Pancoast | Bag-of-audio-words approach for multimedia event classification[END_REF], [START_REF] Lim | Robust sound event classification using lbp-hog based bag-of-audio-words feature representation[END_REF], [START_REF] Lu | Sparse representation based on a bag of spectral exemplars for acoustic event detection[END_REF], [START_REF] Kumar | Audio event detection from acoustic unit occurrence patterns[END_REF]. Taking cues from sound source separation where Non-Negative Matrix Factorization (NMF) have been very successful, NMF has been exploited in a variety of ways for audio event detection [START_REF] Gemmeke | An exemplar-based nmf approach to audio event detection[END_REF], [START_REF] Heittola | Sound event detection in multisource environments using source separation[END_REF]. More recently several deep learning methods, especially using Convolutional Neural Networks [START_REF] Piczak | Environmental sound classification with convolutional neural networks[END_REF], [START_REF] Zhang | Robust sound event recognition using convolutional neural networks[END_REF], [START_REF] Phan | Robust audio event recognition with 1-max pooling convolutional neural networks[END_REF], [START_REF] Hershey | Cnn architectures for large-scale audio classification[END_REF] have been proposed for audio event classification and detection. However, as pointed out earlier, fully supervised learning of sound events cannot be scaled due to difficulties in creating large-scale strongly labeled datasets.

[7] first proposed weak label learning for generic sound events. The authors formulated it as a Multiple Instance Learning (MIL) [START_REF] Zhou | Multi-instance learning: A survey[END_REF] problem and used Support Vector Machine (SVM) and feedforward neural network based MIL methods as learning algorithms. Note that, although the training is done using weakly labeled data where temporal information is not available, the authors showed that temporal localization is possible. MIL methods often suffer from scalability issues, especially iterative methods such as miSVM [START_REF] Andrews | Support vector machines for multiple-instance learning[END_REF] in which an SVM is solved at each iteration. [START_REF] Kumar | Weakly supervised scalable audio content analysis[END_REF] proposed scalable MIL methods for audio event classification using weak labels. The primary idea is to embed bags into vector representations and change the hypothesis space to full supervised methods. In this process, however, temporal localization capabilities are lost.

Several deep neural networks based methods have been recently proposed for weakly supervised audio event detection. A significant chunk of these methods use convolutional neural networks [START_REF] Su | Weakly-supervised audio event detection using event-specific gaussian filters and fully convolutional networks[END_REF], [START_REF]Deep cnn framework for audio event recognition using weakly labeled web data[END_REF], [START_REF] Xu | Attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging[END_REF], [START_REF] Kumar | Knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes[END_REF]. [START_REF]Deep cnn framework for audio event recognition using weakly labeled web data[END_REF] is based on the idea that one can design a CNN to scan and produce outputs at small segments (say 1 second) and then map these segments level outputs to full recording level outputs. Once this mapping is achieved one can compute loss and train the network, as weak labeling gives labels at full recording level. This idea also forms the basis of the approach taken in this work. [START_REF] Su | Weakly-supervised audio event detection using event-specific gaussian filters and fully convolutional networks[END_REF] operates at frame level and combines CNN with event-specific Gaussian filters to produce recording level outputs before computing loss. [START_REF] Xu | Attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging[END_REF] combines CNN with recurrent architecture to incorporate attention in the learning framework. CNN's operate on small (32 milliseconds) duration windows and the outputs from these CNN's are fed into a recurrent neural network to produce recording level outputs for computing loss. Although the idea of having a recurrent architecture following a CNN seems promising, the proposed method in the current form is not feasible for long duration (say several minutes long) audio recordings efficiently. Our CNN based method used in this work is capable of handling long and varying duration audio recordings. Another work [START_REF] Hershey | Cnn architectures for large-scale audio classification[END_REF], ignores the weakly labeled nature of web data and trains well known CNN architectures such as VGG [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF], ResNet [START_REF] He | Deep residual learning for image recognition[END_REF] by making strong label assumptions. However, [START_REF] Kumar | Knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes[END_REF] shows that it is beneficial to treat weakly labeled data as such.

Weak label learning for sound events has also been now incorporated as a task in the annual IEEE challenge on sound events and acoustic scenes (DCASE 2017 [START_REF] Mesaros | Dcase 2017 challenge setup: Tasks, datasets and baseline system[END_REF], DCASE 20186 ). A small subset of Audioset is used in the challenge. Most of the top performing methods on the weak label task in DCASE 2017 7 relied on convolutional neural networks.

The central theme of noise in weak label learning paradigm for sounds is completely missing from the literature to the best of our knowledge. Perhaps the closest related work in this respect is [START_REF] Kumar | Audio event and scene recognition: A unified approach using strongly and weakly labeled data[END_REF]. [START_REF] Kumar | Audio event and scene recognition: A unified approach using strongly and weakly labeled data[END_REF] acknowledges the theme of noise in weakly supervised learning and proposed that it can be addressed by a unified learning framework which learns simultaneously from strongly and weakly labeled data. The authors proposed that this unified learning framework can be formulated as a constraint form of semi-supervised learning. A graph-based constraint semi-supervised learning method is then proposed as the solution. However, a more in-depth analysis; showing how label noise in different forms can manifest itself in weakly supervised learning has not been undertaken. Moreover, the proposed graph-based is hard to scale for a large amount of audio data. For example, we consider experiments on over 360 hours of audio data. The constraint optimization problem described in [START_REF] Kumar | Audio event and scene recognition: A unified approach using strongly and weakly labeled data[END_REF] work can be very difficult to solve on such large datasets.

III. CNN FOR WEAKLY LABELED AUDIO

Our CNN for weakly labeled AED is designed for logmel spectrograms as input. An audio recording is represented by X ∈ R n × m logmel spectrograms. n is the number of logmel frames and m is the number of Mel-filters used in the representation. For the purposed of discussion in this section, we will use m = 128 and hence an audio recording is represented by a time-frequency matrix X ∈ R nX128 . We will use the term "frame" to refer to the i th 128 dimensional time frame, "segment" to represent a small segment of the whole audio recording. In terms of logmel features a segment is a collection of k continuous frames (k×128 matrix). For example, k = 128 represents can represent a 1.5 second segment. The details of logmel features are provided in experiments section.

Given a collection of weakly labeled recordings (X i , y i ), the goal is to design a network architecture which can learn from this weakly labeled dataset. We would expect the network to factor in the fact that the labels are weak. We have a label for the whole recording but that labeled event need not be present in the whole recording. Moreover, the network should be able to handle audio recordings of variable length as the recordings can be a few seconds long or may be even a few minutes long. The primary idea here is to design the network such that we can predict posteriors of the presence of the events at small segments (say 1 or 1.5 seconds), and then map these segment level posteriors to recording level posteriors. Once we have the recording level posteriors we can compute the loss with respect to the available recording level labels and then train the network using backpropagation.

A. CNN Architecture

The ideas outlined in previous paragraph are conceptualized by the network shown in Figure 1, referred to as WAL-Net. The blocks of layers L1 to L5 consists of two convolutional layers followed by a max pooling layer. The convolutional layers consists of batch normalization before non-linear activation function. ReLU (max(0, x)) [START_REF] Nair | Rectified linear units improve restricted boltzmann machines[END_REF] is used in all layers from L1 to L6. 3×3 convolutional filters are used in all layers from L1 to L6. Stride and padding values are fixed to 1. The number of filters employed in different layers is as follows, {L1: 16, L2:32, L3:64, L4:128, L5:256, L6:512 }. Note that L1 to L6 consists of two convolutional layers followed by max pooling. Both convolutional layers in these blocks employ same number of filters. The max pooling operation is applied over a window of size 2 × 2. Logmel inputs are treated as single channel inputs.

The convolutional layers in L1 does not change the height and width of input. The max pooling operation reduces the size by a factor of 2. For example, a recording consisting of 128 logmel frames that is an input of size X ∈ R 1×128×128 , will produce and output of size 16 × 64 × 64 after L1. This design aspect applies to all layer blocks from L1 to L6 and hence 1 × 128 × 128 input will produce a 512 × 2 × 2 output after L6. L7 is a convolutional layer with 1024 filters of size 2 × 2. Once again ReLU activation is used. Stride is again fixed to 1 and no padding is used.

Layer L8 represents segment level output. It is a convolutional layer consisting of C s filters of size 1 × 1. C s is the number of class in the dataset. Sigmoid non-linear activation function is used at this secondary output layer. The final global pooling player, P , maps the segment level outputs from L8 to full recording level outputs. In this case we use average function to perform this mapping. That is the recording-level posterior for each class is the average of the segment-level posteriors for each class.

B. Characteristics of WAL-Net

This network design embodies the different aspects of weakly labeled audio data outlined at the beginning of this section. The network is fully convolutional which allows it to handle recording of variable length. It is designed to predict posteriors at recording level by first predicting class posteriors at small segments. The principle applied here is that we scan through the recording and predict outputs at every small segment. Once we know what happened at segment level, we can use it to assign what will happen at recording level. In principle, this segment to recording level mapping of posteriors can be done by a different functions. For example, one can also use max function. This would imply that the recording level posterior for a class is the highest probability of occurrence of that event across different segments. We use average because empirically we found that it works better than max. It is possible to design other complex mapping function as well.

The network design controls the size of the segments over which posteriors are predicted. The amount by which the segment moves can also be controlled by the network design as per requirement. In the specific case of WAL-Net presented in Fig 1 (and used in this work), the segment size is 128 frames (∼ 1.5 seconds) and the segment can be thought of as moving by 64 frames (∼ 0.75 seconds). Hence, consecutive segments overlap by 50%. The number of segments obtained after layer L8 depends on the duration of the audio recording. For example, one can easily verify that an audio recording consisting of 864 logmel frames (X ∈ R 864×128 ) will produce K = 12 segments at L8. If needed these segment level outputs can give temporal localization of the event as well. Hence, the network is capable of localizing events despite learning from weakly labeled data.

C. Multi Label Training for WAL-Net

An audio recording can have multiple classes present in it. The weakly labeled Audioset, on an average consists of 2.7 labels per recording. Hence, the training procedure needs to consider presence of multiple labels in the recording. The output of WAL-Net gives class specific posteriors for any given input. The binary cross entropy loss with respect to each class is then given by Eq. 1.

l(y c , p c ) = -y c * log(p c ) -(1 -y c ) * log(1 -p c ) (1)
y c is the target output for c th class, 1 if c th event is marked to be present and 0 otherwise. pc = WAL-Net(X) is the network output for the c th class. The training loss is the mean of losses over all classes

L(X, y) = 1 C * Σ Cs c=1 l(y c , p c ). (2)
IV. LABEL NOISES AND CORRESPONDING EXPERIMENTAL DESIGNS A. Labels Density Label density signifies how much of a given audio recording actually contains the the tagged event. In weakly labeled data, we only know whether an event is present or not. We do not know what portion of the recording actually contains the marked event. Hence, the concept of label density and correspondingly label density noise is naturally embodied into weakly label learning. We formally define label density (LD) of a recording R, with respect to an audio event e as

LD e R = Duration (seconds) f or which e ispresent in R Length (seconds) of R (3)
Correspondingly, label density noise (LDN) is defined as

LDN e R = 1 -LD e R .
Label density noise is a measure of weakness of a recording with respect to a given event. In other words, how weak are the labels for a given weakly labeled audio recording. A one-minute long audio recording where a sound event is present for only a couple of seconds is a much more weaker representation of the event, compared to a similar audio recording where the event is present for almost three-quarters of the duration of the recording.

Understanding how label density affects the learning process in weakly supervised paradigm is important. However, designing an evaluation strategy to measure the impact of label density on generalization capabilities of trained models is not straight forward. Any such method would require one to measure label density of in all recordings with respect to each event. This would clearly require manually obtaining the duration for which a given event is present in the audio recording. Clearly, this cannot be done for a large scale dataset such as Audioset. Hence, we consider an alternative view on label density which allows us to empirically study its impact on weakly labeled AED.

The alternate view is based on the intuition that if we mine weakly labeled audio from the web, then the expected density of labels for a given audio event will be lower for long duration audio recordings. In other words, on an average long duration audio recordings will have higher "label density noise" compared to shorter audio recordings.

Taking this alternate view, we design our experiment as follows. We consider Audioset, a large scale weakly labeled dataset for sound events. Audioset provides weak labels for YouTube audio recordings. Weak labeling was manually done on audio recordings of 10 seconds duration (a small fraction are of less than 10 seconds). These 10 seconds audio segments were actually obtained from longer YouTube videos.

Let us represent the Audioset training data by tuples of form

(R i , Y T i id , S i , E i , L i ). R i is the i th recording in Audioset and Y T i id is the YouTube id of R i . S i is the start time of R i in Y T i id and E i is the end time of R i in Y T i id .
That is R i is a portion of the video Y T i id on YouTube, starting at S i and ending at E i . R i 's are weakly labeled (manually). L i represents the set of audio events present in R i . Hence, this labeling tells us that the set of events in L i are somewhere present in Y T i id between S i and E i . We request the readers to take a quick look at the label sets provided by Audioset8 to understand this aspect. R i 's are mostly 10 seconds long recordings and will have label densities likely on the higher side. For each Y T i id in training set, we consider the audio from S i -10 seconds to E i + 10 seconds. Clearly, the set of L i events are present in this audio as well and hence we obtain 30 seconds long weakly labeled audio recordings. Those Y T i id where the total duration of YouTube video is less than 30 seconds, we simply consider the whole recording. We call the obtained weakly labeled set as Audioset-At-30. It is expected that on an average the label density of events in Audioset-At-30 will be lower than Audioset. Similarly, we obtain Audioset-At-60 by considering S i -25 seconds to E i +25 seconds for each (Y T i id , S i , E i , L i ). Once again Y T i id which are of less than 60 seconds are taken entirely. Audioset-At-60 is expected to have even lower label density or higher label density noise. We train our proposed WAL-Net on all three sets and analyze how label density affects the performance. Since our network is designed to handle audio recordings of variable length, varying length of recordings is not a problem.

B. Corrupted Labels Noise

Corrupted labels or wrong labels is possible due to a variety of reasons. Sound events are often hard to interpret which might present difficulties in labeling recordings even when manually done. This is especially true for YouTube quality audio recordings where "signal noise" can create further difficulties in understanding and labeling the audio events. For Audioset, the authors themselves checked a random sample of 10 recordings for each event and provided a confidence on correctness or quality of labels 9 . One can observe that this confidence is less than 90% for several events.

Quality of labels can reduce by a considerable amount when we try to directly mine audio from web and automate the weak labeling by using metadata associated with audios (videos) to assign labels. Both false positive as well as false negative assignment of labels are possible. That is we may mark an event to be present in the recording when its not. Similarly, we might miss the presence of an event and mark the recording to not contain the given sound event. Since one of the major motivation behind weak labels is the fact that we can exploit large amount of audio from web by automatically labeling them and then train large scale models; it is imperative that we take a closer look at this form of label noise.

We analyze this form of label noise by corrupting the labels in Audioset. We treat the labels from Audioset as perfect or at 0% corruption. We note that this is actually not true. However, Audioset has been manually labeled and it is the best one can possibly do for a collection of over 500 sound events. Hence, we consider Audioset as having perfect labels without any noise. We then gradually increase the amount of corrupted labels by manually corrupting the assigned labels in Audioset. More specifically, for a portion of the labeled tuples (Y T i id , S i , E i , L i ), we corrupt L i by changing the events in L i . The corruption process is done in a stratified way such that around r% labels for each event get corrupted. r% includes both false positive and false negative label noises. Moreover, we perform the corruption in a way such that the number of recordings marked to contain the recording remains consistent with respect to original labels. We perform analysis on different values of r. The set of corrupted labels for each r will be released for future works.

C. Weakly Labeled Audio In the Wild

This part tries to understand challenges of label noise in weakly labeled audio in a situation where both of the above forms of label noises can occur in abundance. We directly obtain audio from YouTube for a given sound event and then train our model using these data. We apply a simple but effective filtering approach by retrieving videos based on the search queries of form "<sound event name > sound". Adding the word "sound" leads to a considerable improvement in retrieval of relevant videos on YouTube.

For each event we consider the top 50 retrieved videos (under 4 minutes duration) and mark these to contain the event. If a video is retrieved for multiple events, they are accordingly multi-labeled. We call this training set YouTubewild. In YouTube-wild label noises in all forms occur. Label density can vary from 0 to 1. Since the retrieval is not perfect, marking all top 50 videos to contain the event introduces false positive labeling, implying we are assigning a positive label to the recording even when the event is not present. False negative labels also gets naturally introduced in the process, as a retrieved video V for an event, e 1 , might contain another event, e 2 , as well. But unless V was retrieved for e 2 also, we do not know this and we mark e 2 to be not present in V . For training set, out of the total 527 events, 457 events have recording counts between 59 and 70. This number is 441 for evaluation set.

Several of the audio events in Audioset have vague names and represent an extremely broad meaning. The automated labeling process described in the previous paragraph leads to more or less useless results for these events. Hence, for this part we worked with a smaller subset of events; selecting those which have more definite meaning. That is those for which retrieval leads to somewhat meaningful and relevant set of audio recordings. The selection process also factors in the total number of examples available for the event in Audioset. We select events for which more examples are available in Audioset. We do this because we wanted to analyze how YouTube-wild compares with Audioset, which is manually labeled. It is desirable that we work with events for which higher number of examples are available in Audioset, to better understand where YouTube-wild training stand in comparison to Audioset. YouTube-wild is collected and labeled without manual effort and contains long duration audio recordings, against Audioset, which is manually labeled and weak labels are over relatively short 10 seconds recordings. Since, the source of both is YouTube, signal noise is expected to be similar. The two forms of "label noise", however, are going to make the most difference. More details in experiments section.

V. EXPERIMENTS AND RESULTS

A. Dataset

We use AudioSet [START_REF] Gemmeke | Audio set: An ontology and human-labeled dataset for audio events[END_REF]

B. Acoustic Features, Implementation and Evaluation Metrics

We use Logmel spectrograms as acoustic features for audio recordings. All audio recordings are sampled at 44.1 KHz sampling frequency. A window of ∼ 23ms (1024 points) and window hop size of ∼ 11.5 ms is used. The number of mel bands is set to 128. Hence, each frame of X ∈ R k × 128 corresponds to the logmel representation of a 32 ms window. As mentioned in Section III, WAL-Net is designed to produce outputs for a 128 frame segment moving by 64 frames. This corresponds to a segment size of ∼ 1.5 seconds and segment hop size of ∼ 0.75 seconds.

We use Pytorch 10 deep learning toolkit in our experiments. Hyper parameters such as learning rate is tuned using the validation set. Adam optimization [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] is used for training the networks and model selection (across different epochs) and parameter tuning is done using the validation set.

We use average precision (AP) [START_REF] Buckley | Retrieval evaluation with incomplete information[END_REF] and area under ROC curves for each event (AUC) [START_REF] Fawcett | Roc graphs: Notes and practical considerations for researchers[END_REF] as evaluation metrics. Mean Average Precision (MAP) and Mean Area Under ROC curves (MAUC) over all events are used as overall evaluation metrics. Temporal localization performance numbers are not reported because Audioset does not provide temporal information to compute any suitable metric.

C. Audioset Performance

Table I shows MAP and MAUC on evaluation set over all 527 sound events. For the purpose of comparison we also show reported numbers in [START_REF] Wu | Reducing Model Complexity for DNN Based Large-Scale Audio Classification[END_REF]. Note that the training and evaluation might vary slightly in these two cases as per the availability of YouTube videos when they were downloaded. WAL-Net is able to achieve almost similar MAUC when compared to [START_REF] Wu | Reducing Model Complexity for DNN Based Large-Scale Audio Classification[END_REF]. It is not possible to show the AP and AUC numbers for all 527 events. Table II shows performance numbers for 10 events for which best AP are obtained and 10 events for which worst AP are obtained. Corresponding AUC values are also reported. An important worth noting from Table II is that performance is very high for events which are very specific and have 10 http://pytorch.org/ salient characteristics, such as music, bagpipes, siren whereas ambiguous events such as Harmonic, Burst pop, inside-public space are harder to understand for WAL-Net. The ambiguities in content not only makes it harder for the network to learn these events but most likely the weakly labeled examples of these events in Audioset contains higher signal and label noise.

D. Analyzing Label Density

The experimental setup for this part follows the procedure described in Section IV-A. For the training set in Audioset, we created two additional sets, namely, Audioset-At-30 and Audioset-At-60. Audioset-At-30 and Audioset-At-60 are approximately 180 and 360 hours of audio data respectively. These two additional training sets have higher label density noise compared to original Audioset. We train WAL-Net on original Audioset, Audioset-At-30 and Audioset-At-60. The validation and evaluation sets are kept same as before. As usual validation set was in parameter tuning. The original Audioset gives label density at 10 seconds segment. Hence, we are analyzing the performance of models trained on datasets for which weak labels are available at 10 seconds, 30 seconds and 60 seconds granularity.

Table III shows the effect of label density change in MAP and MAUC numbers. The MAP value decreases by a sharp 12% in relative terms as we move from weak labels at 10 seconds long audio recordings in Audioset to 30 seconds long audio recordings in Audioset-At-30. The drop in performance is evident in both MAP and MAUC numbers. However, going from 30 to seconds leads to an additional drop of only 4% for MAP. No significant change in MAUC is observed. This shows an interesting behavior. As we increase the recording durations from 10 to 30 seconds, the model is not able to learn well from relatively reduced label density. However, increasing audio lengths also increases the total amount of training data and one might attribute this behavior to the increased training data. Larger training data have been known to improve the robustness of deep learning models. This characteristic of deep learning methods starts showing up when we train on Audioset-At-60. Although, the performance does go down due to reduced label density, training on larger amount of data does improve the robustness of WAL-Net and the drop in performance is restricted to a certain extent. Hence, when weak label learning is done on long duration recordings, it is desirable to use large amounts of data to counter the effects of increased label density noise.

Table IV shows 10 events for which maximum drop (relative) in AP is observed for Audioset-At-30 compared to Audioset. For the purpose of showing events in Table IV, we considered only those whose AP is more than the MAP (0.196). Similarly, Table V shows the same for Audioset-At- 60. For these 10 events, on an average around 62% drop in AP is seen for Audioset-At-30 and 65% for Audioset-At-60. Note that the set of events for which maximal drop is observed for Audioset-At-30 and Audioset-At-60 are different. Interestingly, most of the events in these two lists, except for Pink Noise, Music of Bollywood, Music Africa have unique and specific characteristics and are easily identifiable by humans. One can then argue that such events are more prone to suffer from label density noise.

E. Analysis of Labels Corruption

The analysis of label noise in terms of corrupted labels follows the procedure outlined in IV-B. We use 9 different values of r, the level of artificially induced corruptions in labels. Correspondingly, 9 different training sets are obtained. A WAL-Net is trained on each of these training sets. The validation and evaluation sets remain as before. Fig. 3 shows MAP values for different values of r. r = 0 corresponds to the original training set from Audioset.

Adding around 2% noise in labels leads to around 8.7% drop in MAP performance. The model remains fairly robust to additional noise till r = 5. In fact increasing noise labels to around 20% noise. r = 20% leads to an extra drop of only 4% in MAP, compared to the performance at r = 4%. This shows that WAl-Net is robust to a certain extent to label noise. At very high r = 40 and r = 50, as expected a sharp decrease in performance is noted. The MAP at these r are 12.93(-34%) and 12.1(-38%).

Overall, we can state that corrupted labels are important and depending on the amount of label noise, a considerable drop in performance can be observed. Still, a deep learning model like WAL-Net seems robust to a certain extent. Future works on AED using weak labels, especially those relying on YouTube data should factor in the possibility of corrupted labels and model accordingly. As pointed out earlier, even manually labeled dataset like Audioset is not free of label Table VI shows top 10 events for which maximum relative drop in performance is observed. The drop of around 90% for Firecracker is especially noticeable. On an average these 10 events show a drop of 77% in performance. There is drop of around 10.5% in AUC values as well. Note that two events, Cattle bovine and Moo are expected to often co-occur together. For these two events we have almost similar performance and the performance after noise addition is also similar. This is consistent with the design of our labels corruption strategy.

F. Weakly Labeled Audio in the Wild

In this Section, we analyze the situation in which the previous two forms of "label noise" occurs naturally and in fact in abundance, which is going to affect the learning process. We work directly with audio (video) obtained from YouTube and labeled without any manual intervention. The procedure follows the process outlined in Section IV-C. As noted in Section IV-C, we work with only a small subset of events from Audioset, events with salient characteristics and "names" which can be used in query terms to yield at least somewhat relevant results from YouTube.

We selected a total of 40 sound events and obtained training examples for them from YouTube by the process described in Section IV-C. This set, called YouTube-Wild is used as the training set for training WAL-Net. It consists of a total of 1906 videos for 40 sound events, totaling around 60 hours of audio. The average duration of audio recordings is around 111 seconds, with maximum duration of 240 seconds. In comparison, Audioset has mostly 10 seconds long audio recordings. All audio recordings are sampled to 44.1 kHz sampling rate. YouTube-Wild training list will be released and made available for future works in this area.

We train WAL-Net (with C s = 40) on YouTube-wild. We also train WAL-Net (C s = 40) on the subset Audioset training set, which contains audio recordings belonging to only these 40 sound events. We call this subset of of Audioset training set as Audioset-40. The subset of Audioset validation and evaluation sets which contains only recordings belonging to only these 40 events are used as validation and evaluation set in these experiments.

Table VII compares performance on Audioset-40 and YouTube-Wild. One can observe the considerable difference between the performance of these training sets. Audioset-40 is manual labeled dataset and is also expected to have reasonably good label density as recordings are of 10 seconds duration. On the other hand, YouTube-wild is not manually labeled or even verified and is expected to have large number corrupted labels. Moreover, YouTube-wild consists of very long duration recordings (upto 4 minutes) and hence even those where the weak labels are correct, label density noise can be very high. Together these two forms of noises severely affects the learning process. Even though WAL-Net showed reasonably good robustness in the previous sections, we observe that learning sound events from wild web data without any manual intervention is an extremely difficult task. Future works on large scale AED needs to develop algorithms which can address these challenges.

Table VIII shows 10 best and worst performing events for YouTube-Wild. Corresponding Audioset-40 numbers for these events are shown the Table . Even for the 10 event where YouTube-Wild does really well, compared to Audioset-40 an average drop of 55% in AP is noted. At the same time, there are a few events such as Chicken, Rooster among best 10 and Bus, Motorboat and Motorcycle among worst 10 where both sets perform equally well or not.

Table IX shows 10 events for which highest percentage drop in performance is observed. Corresponding AUC values are also shows in parenthesis. As expected the average drop over these cases is very high, greater than 80%. Interestingly, 3 of the classes (Train, Rail Transport, and Railroad Car-Wagon) are broadly expected to fetch similar results from YouTube. For all of these 3 classes performance drop is large.

VI. CONCLUSION

In this paper, we attempted to understand the challenges in large scale AED using weakly labeled data. We first described a CNN based framework to learn from weak labeled audio recordings. The network design embodies several desirable characteristics from the perspective of weakly labeled audio event detection. It can handle recordings of variable length, the network design controls segment sizes over which secondary outputs are produced and can be adjusted. This removes any additional pre-processing step. The network is able to achieve state-of-art performance (using balanced training set) of Audioset.

We then described different ways in which label noise manifests itself in weakly labeled data. We proposed experimental designs which can help study the effect of these factors. In particular, we looked into how label density and labels corruption affects performance. Moreover, we also considered directly mining weakly labeled data from web and compared it with Audioset, which has been manually labeled over shorter audio recordings. The primary motivation behind these is to understand the challenges and help future works on AED with weak labels adapt accordingly. Mining weakly labeled data from web is not easy and one needs to bring in sophisticated algorithms to assign weak labels to the audios. Label density as described in this work naturally comes in weak label learning paradigm. Algorithms should be designed to handle audio recordings with wide range of label densities. Corruption in noise labels is another important factor and again robustness to a certain extent again labels corruption is desirable.

Fig. 1 .

 1 Fig.1. Weak Audio Label (WAL-Net) for Audio Events. The layer blocks from L1 to L6 consists of 2 or 1 convolutional layers followed by max pooling. L8 represents segment level output which is further mapped by the global average pooling layer P to produce recording level output. The loss is then computed with respect to the target recording level labels.

  9 https://research.google.com/audioset/dataset/index.html

Fig. 2 .

 2 Fig. 2. Left: Distribution of labels in training set. Right: Distribution of labels in evaluation set. X axis shows a range of number of examples and the Y axis shows number of events for which number of examples lies in that range.For training set, out of the total 527 events, 457 events have recording counts between 59 and 70. This number is 441 for evaluation set.

Fig. 3 .

 3 Fig. 3. Effect of corruption of labels on performance. x-axis represents corruption level r in %. y-axis show MAP and % reduction in MAP compared to no corruption.

  in our experiments, a large scale weakly labeled dataset consisting of 527 sound events. The dataset comes pre-divided into 3 sets. We use the balanced training set as the training set for our experiments. The balanced training set consists of a total of 21508 audio recordings. The actual balanced training set available from Audioset is slightly larger, some of the videos were not available for download at the time we downloaded them. Most of the recordings are of 10 seconds duration, although a few are less than 10 seconds as well. The total training data is approximately 60 hours of data. We use the Eval set from Audioset as the test set in our experiments. The Eval set consists of a total of 19,789 recordings. The training and test sets consists of atleast 59 examples per class. We sample out a collection of 12,676 recordings from Unbalanced set of Audioset to use as validation set in our experiments. This validation set consists of atleast 30 examples per class. Audioset is a multi-label dataset and on an average 2.7 labels per recording are present. The distribution of labels across different events is unbalanced. Fig 2 shows this imbalance.

TABLE III EFFECT

 III OF LABEL DENSITY ON PERFORMANCE

	Training Set	MAP	MAUC
	Audioset	0.196	0.925
	Audioset-At-30 0.172 (-12%) 0.904 (-2%)
	Audioset-At-60 0.165 (-16%) 0.908 (-2%)

TABLE IV 10

 IV AUDIO EVENTS WITH HIGHEST DROP IN AP FOR AUDIOSET-AT-30 (W.R.T AUDIOSET)

	Events	Audioset -AP, (AUC) Audioset-At-30 -AP, (AUC)	AP Drop (% Drop)
	Speech synthesizer	0.225, (0.953)	0.037, (0.882)	0.187, (83.506)
	Air horn truck horn	0.290, (0.979)	0.051, (0.950)	0.239, (82.310)
	Vehicle horn, honking 0.238, (0.955)	0.084, (0.923)	0.155, (64.913)
	Afrobeat	0.261, (0.977)	0.104, (0.967)	0.157, (60.088)
	Whip	0.274, (0.927)	0.112, (0.876)	0.162, (59.117)
	Chopping (food)	0.285, (0.920)	0.117, (0.886)	0.168, (58.887)
	Music of Bollywood	0.309, (0.933)	0.140, (0.933)	0.169, (54.765)
	Sizzle	0.339, (0.976)	0.155, (0.958)	0.183, (54.122)
	Toot	0.290, (0.963)	0.137, (0.961)	0.153, (52.813)
	Pour	0.238, (0.957)	0.115, (0.940)	0.123, (51.717)
	Mean	0.275, (0.954)	0.105, (0.927)	0.170, (62.224)

TABLE V 10

 V AUDIO EVENTS WITH HIGHEST DROP IN AP FOR AUDIOSET-AT-60 (W.R.T AUDIOSET)

	Sound Events	Audioset -AP, (AUC) Audioset-At-60 -AP, (AUC) AP Drop (% Drop)
	Chopping (food)	0.285, (0.920)	0.064, (0.871)	0.220, (77.348)
	Pink noise	0.349, (0.974)	0.093, (0.942)	0.257, (73.448)
	Cash register	0.205, (0.917)	0.056, (0.918)	0.149, (72.722)
	Whip	0.274, (0.927)	0.078, (0.877)	0.196, (71.705)
	Music of Africa	0.225, (0.966)	0.082, (0.919)	0.143, (63.512)
	Keyboard (musical) 0.303, (0.955)	0.116, (0.907)	0.186, (61.524)
	Afrobeat	0.261, (0.977)	0.102, (0.955)	0.159, (61.038)
	Speech synthesizer	0.225, (0.953)	0.089, (0.905)	0.136, (60.401)
	Electronic organ	0.205, (0.916)	0.089, (0.847)	0.116, (56.491)
	Artillery fire	0.218, (0.963)	0.102, (0.939)	0.117, (53.482)
	Mean	0.255, (0.947)	0.087, (0.908)	0.168, (65.167)

TABLE VI 10

 VI EVENTS WITH HIGHEST DROP IN AP (FOR r = 30% LABEL NOISE)

	Events	AP, AUC @ No Noise AP, AUC @ 30 % Noise	Drop in AP, (% Drop )
	Firecracker	0.223, (0.964)	0.023, (0.814)	0.200, (89.737)
	Cough	0.218, (0.952)	0.038, (0.831)	0.180, (82.771)
	Chainsaw	0.395, (0.947)	0.079, (0.861)	0.316, (79.995)
	Acoustic guitar 0.259, (0.967)	0.052, (0.857)	0.207, (79.972)
	Waterfall	0.224, (0.975)	0.054, (0.918)	0.170, (75.737)
	Car	0.214, (0.921)	0.056, (0.751)	0.158, (73.755)
	Sizzle	0.339, (0.976)	0.091, (0.896)	0.248, (73.049)
	Cattle bovine	0.214, (0.930)	0.059, (0.854)	0.155, (72.506)
	Moo	0.219, (0.923)	0.061, (0.880)	0.159, (72.400)
	Lawn mower	0.204, (0.972)	0.058, (0.856)	0.146, (71.685)
	Mean	0.251, (0.953)	0.057, (0.852)	0.194, (77.161)

TABLE VII WEAKLY

 VII LABELED AUDIO IN THE WILD. COMPARISON WITH AUDIOSET

	Training Set	MAP MAUC
	Audioset-40	0.419	0.913
	YouTube-Wild 0.127	0.70-
	noise and it is expected that any other large scale weakly
	labeled dataset will have the same problem. Hence, training
	algorithms should be built around label noise in datasets.

TABLE IX 10

 IX EVENTS WITH HIGHEST RELATIVE DROP IN PERFORMANCE

	Events	Audioset-40 -AP (AUC) YouTube-Wild AP Drop (% Drop)
	Train	0.427, (0.925)	0.035, (0.476)	0.392, (91.861)
	Violin fiddle	0.446, (0.945)	0.041, (0.692)	0.405, (90.727)
	Rail transport	0.441, (0.929)	0.042, (0.564)	0.399, (90.386)
	Singing	0.659, (0.947)	0.089, (0.529)	0.569, (86.420)
	Keyboard (musical)	0.568, (0.949)	0.084, (0.735)	0.484, (85.168)
	Railroad car, train wagon 0.451, (0.934)	0.071, (0.697)	0.380, (84.313)
	Water	0.616, (0.935)	0.118, (0.621)	0.498, (80.817)
	Bass drum	0.541, (0.973)	0.119, (0.782)	0.422, (78.070)
	Cymbal	0.577, (0.977)	0.151, (0.833)	0.426, (73.911)
	Pigeon, dove	0.526, (0.956)	0.140, (0.771)	0.386, (73.388)
	Mean	0.525, (0.947)	0.089, (0.670)	0.436, (83.506)

http://blog-idcuk.com/sound-recognition-as-a-key-strategic-technologyfor-artificial-intelligence/

https://research.google.com/audioset/dataset/index.html

http://blog-idcuk.com/sound-recognition-as-a-key-strategic-technologyfor-artificial-intelligence/

http://dcase.community/

http://www.cs.tut.fi/sgn/arg/dcase2017/challenge/task-large-scale-soundevent-detection-results

https://research.google.com/audioset/download.html