Irzaman Irzaman

Epa Yunus Pebriyanto

Rosidah Apipah

Iman Noor

Irzaman

Epa Rosidah Apipah

Ahmad Alkadri

Characterization of Optical and Structural of Lanthanum Doped LiTaO3 Thin Films

Keywords: Lithium tantalite, lanthanum dopant, optical properties, structure, thin films

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Introduction

Development of Lithium Tantalate (LiTaO 3 ) films can be carried out by using various methods, such as Chemical Solution Deposition (CSD), Metal Organic Chemical Vapor Deposition (MOCVD), rf sputtering, and Pulsed Laser Ablation Deposition (PLAD) [START_REF] Kushibiki | Quantitative characterization of proton-exchanged layers in LiTaO 3 optoelectronic devices by line-focus-beam acoustic microscopy[END_REF], and it does not need complicated treatment [START_REF] Kushibiki | Quantitative characterization of proton-exchanged layers in LiTaO 3 optoelectronic devices by line-focus-beam acoustic microscopy[END_REF][START_REF] Blanton | Sun: X-ray diffraction characterization of multilayer epitaxial thin films deposited on (0001) sapphire[END_REF]. LiTaO 3 is one of the widely used optoelectric materials to its ferroelectric, piezoelectric, and pyroelectric properties [1,3-8,15-16,]. It is also an attractive materials for integrated-optic applications due to its nonlinear optical properties [START_REF] Tao | Influence of chemical reduction on optical and electrical properties of LiTaO 3 crystal[END_REF][START_REF] Youssel | Characterization of LiTaO 3 thin films fabricated by sol-gel technique[END_REF][START_REF] Amir | Polyvinilpyrrolidone/ polyaniline composite based 36 o YX LiTaO 3 surface acoustic wave H 2 gas sensor[END_REF][START_REF] Norkus | Uncooled linear arrays based on LiTaO 3[END_REF][START_REF] Bruce | Ferroelectric epitaxial thin films for integrated optics[END_REF], large electroptic and piezoelectric coefficient and its superior resistance to laser-induced optical damage [START_REF] Stenger | Thin film lithium tantalate (TFLT TM ) pyroelectric detectors[END_REF][START_REF] Chan | Effects of membrane thickness on the pyroelectric properties of LiTaO 3 thin film IR detectors[END_REF][START_REF] Cassons | Electro-optic coefficients of lithium tantalite at near-infrared wavelengths[END_REF][START_REF] Yakushev | Investigation of the structural, optical and electrical properties of Cu3BiS3 semiconducting thin films[END_REF][START_REF] Capek | Raman studies of ferroelectric domain walls in lithium tantalite and niobate[END_REF][START_REF] Irzaman | Physical and piroelectric properties of tantalum oxide doped lead zirconium titanate Pb 0,995 (Zr 0,525 Ti-0,010 )O 3 thin films and its application for IR sensor[END_REF][START_REF] Irzaman | Tetragonal to cubic phase transformation in tantalum oxide doped PZT ceramic[END_REF][START_REF] Irzaman | Surface rougness and grain size characterization of effect of annealing temperature for growth gallium and tantalum doped Ba 0,5 SR 0,5 TiO 3 thin film[END_REF][START_REF] Kurniawan | and Irzaman: Development and application of Ba0.5Sr0.5TiO 3 (BST) thin film as temperature sensor for satellite technology[END_REF]. In other hand, it is a ferroelectric crystal which undergoes high Curie temperature of 608 • C and it also has high melting temperature at 1650 • C [START_REF] Irzaman | Effect of annealing times for LiTaO 3 thin films on structure, nano scale grain size and band gap[END_REF].

However, most of the techniques used to make LiTaO 3 are not only expensive but also involve difficult stoichiometric quantification and complex equipment. Sol-gel processing [START_REF] Youssel | Characterization of LiTaO 3 thin films fabricated by sol-gel technique[END_REF], a solution-based method, is interesting due to its ease of production and lower cost, and large area thin film. However, the commonly used alkoxide precursors to produce LiTaO 3 films by sol-gel method are highly reactive and require careful control of the hydrolysis-condensation reaction [START_REF] Gonzalez | Effect of thermal treatment temperature on the crystallinity and morphology of LiTaO 3 thin films prepared from polymeric precursor method[END_REF].

In this research, LiTaO 3 thin films were coated on p-type silicon substrates by using CSD method, followed by spin-coating technique and annealed at the temperature of 550 • C then characterized using X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV-Vis Wave Analyzer, and Particle Size Analyzer (PSA). This research aims to manufacture the lanthanum doped LiTaO 3 thin films and determine its optical and structural properties.

Experimental

The thin films preparation was started by cutting the Si substrate with the size of 8 mm × 8 mm, each as many as 5 pieces. Then, the substrates were cleaned using aqua bidest and dried [START_REF] Irzaman | Physical and piroelectric properties of tantalum oxide doped lead zirconium titanate Pb 0,995 (Zr 0,525 Ti-0,010 )O 3 thin films and its application for IR sensor[END_REF][START_REF] Irzaman | Tetragonal to cubic phase transformation in tantalum oxide doped PZT ceramic[END_REF][START_REF] Khan | Optical and structural properties of ZnO thin films fabricated by sol-gel method[END_REF]. In this case, three LiTaO 3 solutions were prepared using CSD (Chemical Solution Deposition) method. The first solution was prepared by mixing 0.1650 gram of LiCH 3 COO and 0.5524 gram of Ta 2 O 5 which were soluted inside 2.5 ml of 2-metoxy methanol which called undoped LiTaO 3 solution. The second solution was prepared by mixing 0.1650 gram of LiCH 3 COO and 0.5524 gram of Ta 2 O 5 which were soluted LiTaO 3 2.5 ml of 2-metoxy methanol with the addition of 0.0295 gram of La 2 O 5 as dopant which called 5% lanthanum doped LiTaO 3 solution. Afterwards, the third solution was prepared by mixing 0.1650 gram of LiCH 3 COO and 0.5524 gram of Ta 2 O 5 which were soluted inside 2.5 ml of 2-metoxy methanol with the addition of 0.0590 gram of La 2 O 5 which called 10% lanthanum doped LiTaO 3 solution.

After the preparation of those three solutions, they were sonificated for 90 minutes using Branson 2510. Afterwards, the solution was dropped towards the substrate's surface on spin coating rotator with speed of 3000 rpm, conducted twice. The remaining solution then dried at 80 • C for 24 hours, then characterized using FTIR and PSA. The dropped substrate was then annealed using Furnace with the increasing rate of temperature at 1.7 • C/minute, started from room temperature until it reaches 550 • C and held constantly for 12.5 hours, and then cooled down into room temperature. The formed thin films were characterized using XRD and Optical Ocean USB 2000.

Results and Discussion

XRD Pattern

Figure 1 shows the XRD pattern of LiTaO 3 thin films with p-type Si (100) substrate after annealing at 550 • C. The data were obtained with interval of 0.02 o in the range of 20 o -80 o [START_REF] Kassim | XRD and AFM studies of ZnS thin films produced by electrodeposition method[END_REF][START_REF] Rajesh | XRD, optical and AFM studies on pristine and partially iodized Ag thin film[END_REF]. These three patterns show a peak at 2 θ = 70 • because the used substrate is silicon.

XRD pattern shows that LiTaO 3 thin films have both crystalline and amorphous structures. Undoped LiTaO 3 thin films have mixed structure with lattice parameter of a = 5.13 Å and c = 13.54 Å. Meanwhile, for 5% La doped LiTaO 3 thin films, the structure is not significantly different with the undoped. However, it has smaller lattice parameter of a = 4.92 Å and c = 14.19 Å. Moreover, for 10% La 2 O 5 doped LiTaO 3 thin films, there is a peak (crystalline) of LiTaO 3 with lattice parameter of a = 5.11 Å and c = 13.30 Å.

Decreasing in crystal size is also influenced by the radii of its constituent ions. Ionic radii of Li + , Ta 5+ and La 3+ are 0.90 Å, 0.78 Å and 1.172 Å, respectively. It can be seen that the ionic radius of La 3+ is closer to that of Li + so that La 3+ can occupy the positions of Li + in the crystal structure. The difference of Ionic radii between dopant and replaced ion affects the formation of spinel phase. This leads to crystal size decreasing which is caused by the existence of dopant cations in the structure of LiTaO 3 .

FTIR Results

The results show the existence of stretching vibration of OH group, C = C aromatic bonding, Li-O bonding, and Ta-O bonding at wave numbers of 3100-3900 cm -1 , 1650-1450 cm -1 , 1440-1420 cm -1 , and 610-945 cm -1 , respectively [START_REF] Lee | SR: FTIR spectral characterization of thin film coatings of oleic acid on glasses: I. coatings on glasses from ethyl alcohol[END_REF]. According to these results, it is implied that there is a change in absorbance value of LiTaO 3 for each of dopant addition. Thus, the absorption value tends to increase with the increasing in dopant concentration.

Optical Analysis

Energy Gap.

The results show the energy gap of undoped, 5% La doped, and 10% La doped thin films are 2.550 eV, 2.020 eV, and 2.199 eV, respectively (Figs. 345). It can be concluded that the higher dopant concentration leads to the lower energy gap, which means the electron will be easier to move from the valence band area into the conduction band.

In this research, the overall values of energy gap of thin films from silicon substrate are in the range of 2.0-3.45 eV for all concentrations. According to other researches, the energy gap of pure silicon is in the range of 1.0-1.3 eV . Silicon itself is usually used as semiconductor material and has been applied on many electronic devices for its unique energy gap [START_REF] Cressler | Circuits and Applications Using Silicon Heterostructure Devices[END_REF][START_REF] Diebold | Handbook of Silicon Semiconductor Metrology[END_REF]. In this research, the usage of Si as substrate makes a medium level of energy gap, makes the obtained thin films are capable to be applied in high-voltation electronic components [START_REF] Millán | Wide band gap semiconductor devices for power electronics[END_REF].

From the results, it is also found that there is energy gap increasing from 5% to 10% dopant. According to several researches, energy gap can increase after the dopant addition [START_REF] Cressler | Circuits and Applications Using Silicon Heterostructure Devices[END_REF][START_REF] Diebold | Handbook of Silicon Semiconductor Metrology[END_REF][START_REF] Zhu | Band gap of narrowing titanium oxide semiconductors by noncompensated anion-cation codoping for enhanced visible-light photoactivity[END_REF][START_REF] Ahmed | Band gap narrowing and fluorescence properties of nickel doped SnO 2 nanoparticles[END_REF][START_REF] Chakraborty | Experimental and theoretical investigations of structural and optical properties of copper doped ZnO nanorods[END_REF][START_REF] Lee | Study of diluted magnetic semiconductor: Co-doped ZnO[END_REF][START_REF] Chu | Physics and Properties of Narrow Gap Semiconductors (Microdevices)[END_REF][START_REF] Hamberg | Band-gap widening in heavily Sn-doped In 2 O 3[END_REF][START_REF] Wu | Effects of the narrow band gap on the properties of InN[END_REF][START_REF] Jain | Band gap widening and narrowing in moderately and heavily doped n-ZnO films[END_REF][START_REF] Walsh | Origins of band-gap renormalization in degenerately doped semiconductors[END_REF]. This is the result of Burstein-Moss effect, increasing in band gap value of a thin film-doped semiconductor due to their electrons in the conduction band push the fermi level [START_REF] Manser | Band filling with free charge carriers in organometal halide perovskites[END_REF] which lead to the increasing band gap.

Refractive

Index. The addition of 5% La dopant significantly increases the refractive index from the undoped thin films. However, 10% La dopant decreases the refractive index (Figure 6). Previous researches [START_REF] Butov | Refractive index dispersion of doped silica for fiber optics[END_REF][START_REF] Alwan | Refractive index dispersion and optical properties of dye-doped polystyrene films[END_REF] showed that the refractive index tends to increase with the increasing in dopant concentration, which is caused by the occurrence of disorder in structure, changes in stoichiometry, and internal strain caused by polarizability. Consequently, if containing dopant materials have high polarizability, the refractive index will thus increase [START_REF] Sharma | Linear and nonlinear refractive index of As-Se-Ge and Bi doped As-Se-Ge thin films[END_REF]. In addition, the phenomenon in dopant increasing decreases the refractive index may be caused by the thinning of the material or the widening of energy gap [START_REF] Krowne | Physics of Negative Refraction and Negative Index Materials[END_REF][START_REF] Hotoleanu | Highly Doped Fiber Technology[END_REF]. From the previously observed energy gaps, it can be seen that there was indeed an increase of energy gap from thin film with 5% dopant to 10% dopant, so that it is likely the cause of decreasing in refractive index.

PSA

To determine the particle size of LiTaO 3 thin films, particle size analyzer (PSA) device with scale of 0.6nm-7μm was used. Figure 7 shows that the LiTaO 3 thin films have different particle sizes. Undoped LiTaO 3 thin films have the largest particle size range, which is 745.33 nm. Meanwhile, the particle size of 5% and 10% La doped LiTaO 3 thin films were 372.84 nm and 296.98 nm, respectively. These results indicate that the higher dopant concentration, the smaller the particle size.

Conclusion

The addition of lanthanum dopant ignites the formation of LiTaO 5 crystal, especially in the addition of 10% lanthanum dopant with the hexagonal-shaped crystals. From FTIR, the higher the dopant concentration given, the larger the absorption of Ta-O and Li-O. Moreover, from PSA, it is found that the dopant addition leads to smaller particle size. The optical measurements show that the higher the dopant concentration leads to the lower the energy gap and the higher the refractive index of thin films.

Figure 1 .

 1 Figure 1. the XRD pattern of LiTaO 3 thin film with p type Si (100) substrate after annealing at 550 • C.

Figure 2 .

 2 Figure 2. The FTIR spectra of LiTaO 3 thin films.

Figure 3 .

 3 Figure 3. Energy gap of undoped thin films.

Figure 4 .

 4 Figure 4. Energy gap of 5% lanthanum doped thin films.

Figure 5 .

 5 Figure 5. Energy gap of 10% lanthanum doped thin films.

Figure 6 .

 6 Figure 6. Refractive index of undoped, 5% La doped, and 10% La doped LiTO 3 thin films.

Figure 7 .

 7 Figure 7. The particle size of LiTaO 3 thin films at various mole fractions.

Funding

This work was funded by Kerjasama Luar Negeri dan Publikasi Internasional, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia under contract No. 082/SP2H/UPL/DIT.LITABMAS/II/2015.


Samir Saoudi

Mahmoud Ammar
email: mahmoud.ammar@enit.rnu.tn

Raphael Visoz
email: raphael.visoz@francetelecom.com

Iterative Soft Multipath Interference Cancellation Assisted by Hybrid ARQ with Constellation rearrangement for HSDPA System

High Speed Downlink Packet Access (HSDPA) is the major evolution of UMTS downlink. It provides paek data rates up to 10.8 Mbps. This is reached by radio link adaptivity, which includes fast scheduling, adaptive modulation and coding (AMC), and hybrid automatic reapeat request (HARQ). The performance of AMC suffers from multipath interference introduced by the channel especially when using 16-QAM modulation. This is because of the variation in bit reliabilities caused by the bitmapping onto the signal contellation. In this paper, we present a soft iterative multipath interference canceller (MPIC) for suppressing the multipath interference. We also propose to use HARQ Chase combining with the constellation rearrangement technique in joint application with MPIC. This can equalize the variation of bit reliabilities by using different mapping rules over HARQ transmissions. It is proven that the performance of adaptive modulation and coding is significantly improved after only two HARQ transmissions.

I. INTRODUCTION

The major evolotion of the UMTS standard in recent year is the high speed downlink packet access (HSDPA). It is designed to provide services at high data rates. The use of multicode transmission principle with adaptive modulation and coding (AMC) as well as hybrid automatic repeat request (HARQ) makes it possible to reach peak data rates up to 10.8 Mbps.

In the AMC technique, the base station adapts the modulation and the coding rate to the channel conditions. The coding rates are obtained by the puncturing operation at the output of the turbo code. The modulation is selected between QPSK and 16-QAM. The use of high order modulation (16-QAM) increase significantly the bit-rate. However, this type of modulation is very sensitive to interference. This leads to a severe performance degradation in mutlipath fading channel due to the multipath interference. As a solution to mitigate the performance degradation when using 16-QAM modulation is the implementation of a multipath interference canceller (MPIC) at the receiver. Through last years, many studies were carried out on the MPIC. In [START_REF] Higuchi | Multipath Interference Canceller for High-Speed Packet Transmission with Adaptive Modulation and Coding Scheme in W-CDMA Forward Link[END_REF], authors proposed a multipath interference canceller (MPIC) wich generate the multipath interference based on hard-decision results at the output of the Rake receiver. Although the interference rejection weight used in [START_REF] Higuchi | Multipath Interference Canceller for High-Speed Packet Transmission with Adaptive Modulation and Coding Scheme in W-CDMA Forward Link[END_REF] reduces the impact of generation error of the MPI replica, the optimum of this replica cannot be generated. A soft MPIC combined with HARQ were proposed in [START_REF] Miki | Multipath Interference Canceller Using Soft-decision Replica Combined with Hybrid ARQ in W-CDMA Forward Link[END_REF]. It was proven that more accurate replica is genareted. In this paper, we propose an iterative soft MPIC combined with HARQ Chase combining and constellation rearrangement. In our approach, more relible packets are delivered to turbo decoder by changing the signal constellation mapping from transmission to transmission and making soft combining after interference cancellation. This decrease significantly the data-decoding error. The paper is orgonized as follows. In section II, we describe the principle of the iterative soft MPIC. We present the iterative soft MPIC combined with Chase combining and constellation rearrangement in section III. In section IV, we present the simultor configuration. After, simulation results and discussion are given is section V, concluding remarks are made in section VI.

II. ITERATIVE SOFT MULTIPATH INTERFERENCE

CANCELLER

A. Principle of iterative soft MPIC

The iterative soft multipath interference canceller used in this work is presented in Fig. 1. The interference is generated using a soft symbol estimation after the SISO demapper. For multicode DS-CDMA system, the received signal can be written as

r(t) = L l=1 g l (t) K k=1 s k (t -τ l ).c k (t -τ l ) + n(t) (1)
where g l and τ l are the complex channel cefficient and the time delay of l th path, L and K are the number of paths and the number of physical channels respectivelly, s k and c k represent the modulated signal and spreading code of the k th physical channel, and n(t) is the complex AWGN noise. In this paper we assumed perfect channel knowledge.

The iterative soft MPIC works in an iterative manner. Its purpose is to eliminate the multipath interference of each Rake finger. For each iteration, the MPIC estimates the multipath interference and substract it from the received signal at the input of each finger which then performs descramling, despreading, and apply the channel conjugate. After that, a maximum ratio combining (MRC) is done for all fingers outputs [START_REF] Silva | Multipath interference canceller for high speed downlink packet access in enhanced UMTS networks[END_REF], [START_REF] Ko | Performance analysis of a multistage MPIC in 16-QAM CDMA systems over multipath Rayleigh fading channels[END_REF]. The combined sequence is then taken by the SISO demapper to estimate the log-likelihood ratios (LLR) of transmitted bits. The demapper output is delivered to a soft symbols estiamtion function in order to reconstruct the data modulated sequence. This reconstructed sequence is spread, scrambled and processed by a channel emulator which generates all paths interference contributions [START_REF] Asai | Field experiments on miltipath interference canceller associated with AMC in HSDPA[END_REF], [START_REF] Yu | A New Multipath Interference Mitigation Technique for High-Speed Packet Transmission in WCDMA Downlink[END_REF]. This multipath interferences are stored in a memory to be used in succeding iterations. At p t h stage, the Rake combined sequence of the n th symbol of the k th physical channel is writen as

z (p) k (n) = L l=1 y (p) k,l (n). (g * l ) (p) (2)
The estimated multipath interference (MPI) associated with the l th path at p th iteration can be expressed as

Î(p) l (t -τ l ) = g (p-1) l (t). K k=1 s(p-1) k (t -τ l ).c k (t -τ l ) (3)
The input signal to the l th finger in the p th (p ≥ 2) iteration is represented as

r (p) l = r(t) - L j=1 j =l Î(p-1) j (t -τ j ) (4)
At the final iteration the soft output sequence of the MPIC is delivered to the turbo decoder to recover the transmitted information sequence.

B. Soft symbol estimation

Note

s = [s 1 , s 2 , ..., s M ] the modulated data sequence formed after the bit to sym- bol mapping of the data sequence b = [b 1,1 , b 1,2 , ..., b 1,m , ..., b i,1 , b i,2 , ..., b i,m , ..., b M,1 , b M,2 , ..., b M,m ].
M is the number of modulated symbols, and m = 4 in case of 16-QAM modulation. The LLR of the i th bit of the n th symbol at p th iteration is expressed as

b(p) k (n, i)) = ln P r(b k (n, i) = +1|z (p) k (n)) P r(b k (n, i) = -1|z (p) k (n)) (5)
Note that the LLR of each bit is updated from iteration to iteration. This helps to generate a more accurate soft MPI replica. At p th iteration and for k th physical channel, the n th estimated soft modulation symbol, s(p) k (n), is calculated by taking the mathematical expectation of the data symbol, s k (n), given the output of the SISO demapper. It is written as

s(p) k (n) = E{s k (n)| b(p) k (n, 1), ..., b(p) k (n, 4)} = s k (n)∈A s k (n)P r(s k (n)| b(p) k (n, 1), ..., b(p) k (n, 4)) , (6)
where P r(.) is the probability. The estimated soft symbol, s(p) k (n) can be rewriten as

s(p) k (n) = s(p) k,I (n) + js (p) k,Q (n), (7)
Where s(p) k,I (n) and s(p) k,Q (n) are the in-phase and quadraturephase components of s(p) k (n) wich are expressed as

       s(p) k,I (n) = -1 √ 5 tanh( b(p) k (n, 1)) 2 + tanh( b(p) k (n, 3)) s(p) k,Q (n) = -1 √ 5 tanh( b(p) k (n, 2)) 2 + tanh( b(p) k (n, 4)) (8)
By substituting s(p) k (n) into (3), we obtain the soft multipath interference replica.

III. SOFT ITERATIVE MPIC COMBINED WITH HARQ AND CONSTELLATION REARRANGEMENT

A. Soft iterative MPIC with HARQ

The HSDPA introduced the HARQ technique as an other link adaptation in order to enhance the AMC performance. Fig. 2 shows the principle of HARQ with interference cancellation. The received packet is first processed by the iterative soft MPIC for interference cancellation. Then, it is delivered to the turbo decoder. A CRC detector is used for error detection. When the packet is erroneous, it is saved at the receiver and a NACK is sent to the transmetter which retransmits the packet. At the receiver, the retransmitted packet is combined whith the orignal one before turbo decoding.

The most known of HARQ algorithms is Chase combining which is fist proposed in [START_REF] Chase | Code Combining-A Maximum-Likelihood Decoding Approach for Combining an Arbitrary Number of Noisy Packets[END_REF]. It was proven that Chase approach offers time diversity and provides a significant signal to noise ratio gain after retransmissions combining. In Chase combining, retransmissions are identical to the original transmission [START_REF] Frenger | Performance comparison of HARQ with Chase combining and incremental redundancy for HSDPA[END_REF]. In addition to its reduced complexity, Chase combining requires a small memory.

B. Constellation rearrangement

In [START_REF] Bliudze | On optimal hybrid ARQ control schemes for HSDPA with 16QAM[END_REF], it has been proven that there are variations in the bit reliabilities for 16-QAM modulation. These variations depend on the significance of the bit within a symbol: the two most significant bits are more relible than the two least significant bits [START_REF] Dottling | Incremental redundancy and bit-mapping techniques for high speed downlink packet access[END_REF], [START_REF] Bliudze | On optimal hybrid ARQ control schemes for HSDPA with 16QAM[END_REF]. In case of Chase combining employing the same signal constellation mapping for all transmissions, the variations in bit reliabilities remain when soft-combining the received transmissions at bit level.

In this paper we propose an interworking of Chase combining and constellation rearrangement (CoRe) technique in order to combat the reliability variation problem. The constellation rearrangement averages the bit reliabilities by employing different signal mapping constellations for the HARQ transmissions (see Fig. 2). In this manner, some less protected bits becomes more protected. The bits within a 16-QAM symbol at the input of constellation rearrangement are denoted here by i 1 q 1 i 2 q 2 . The obtained signal constellation mappings are summarized in TABLE I [START_REF] Gpp | Multiplexing and channel coding (FDD)[END_REF]. The mapping #1 represents the

Mapping

Output bit sequence High reliable bits Mapping #1

i 1 q 1 i 2 q 2 i 1 q 1 Mapping #2 i 2 q 2 i 1 i 2 i 2 q 2 Mapping #3 i 1 q 2 i 2 q 2 i 1 q 2 Mapping #4 i 2 q 2 i 1 q 1 i 2 q 2
standard 16-QAM constellation (no rearrangement is made). The over-score on bits in TABLE I indicates that the logical values of this bits changes ( 1 = 0 and 0 = 1).

IV. SIMULATOR CONFIGURATION

For testing our scheme, we implemented the HSDPA link level simulator. It computes the resisuel BLER (Block Error Rate) and throughput for each SNR using monte carlo method. The throughput is given by this expression :

T hroughput = D.(1 -BLER (HARQ) ), ( 9
)
where D is the information bit rate and BLER (HARQ) is the block error rate which takes into account Hybrid ARQ transmissions. At the transmitter, every function is performed according to 3GPP technical specifications [START_REF] Gpp | Multiplexing and channel coding (FDD)[END_REF], [START_REF] Gpp | Spreading and modulation (FDD)[END_REF].

At the receiver, a Rake receiver using MRC (Maximum Ratio Combining) was applied to recover the transmitted signal. The iterative soft multpath interference canceller (MPIC) was implemented using soft interfernce estiamation and 4 iterations. It includes the soft output demapper for updating the soft bits values through MPIC iterations. The turbo decoding uses the Max-Log-MAP algorithm with 10 iterations. The channel model utilizes the profile of ITU-Pedestrian B channel with the speed of 3 Km/h (see Fig. 3). The receiver assumed a perfect channel knowledge. For HARQ, the Chase combining approach with constellation rearrangement technique was used. The maximum number of HARQ transmissions is fixed to 2. A delay of 12 ms is used between HARQ transmissions. The simulations were run using the radio link parameters summarized in TABLE II.

SN R = 10 log 10 (K E s σ 2 ) ( 10
)
where K is the number of codes (physical channels), E s is the symbol energy and σ 2 represents the noise variance. BLER results of CQI 16, CQI 18 and CQI 22 are presented in Fig. 4, Fig. 5 and Fig. 6 respectively. We see that considerable parformance gain is offered by MPIC when combined with Chase combining. An additionnal performance gain is obtained with the constellation rearrangement which , in case of CQI 16 for example, achieved almost 2 dB gain over the technique using the interworking of MPIC and Chase combining without CoRe. For CQI 18, with the Rake, we BLER results for CQI 23 and CQI 24 are shown in Fig. 7 and Fig. 8 respectively. We see that for CQI 23 the error floor observed in MPIC curve, is removed by HARQ, whereas the HARQ curve for CQI 24 have a lowered floor compared The throuput results of these CQIs are presented by Fig. 9 for CQI 16 and Fig. 10 for CQI 18. By analyzing these results, we observe that the throuput is signficantly improved at low signal to noise ratio.

VI. CONCLUSIONS

In this paper, we have shown that the performance of HSDPA CQIs modes using 16-QAM modulation are not very well in multipath environment. This is due to high sensitivity of this modulation to interference. A soft iterative multipath interference canceller was used for suppressing this interference. It was shown that this intereference canceller resulted to a considerable performance enhancement. Besides, the use of HARQ technique and constellation rearrangement offered a great performance gain.

Fig. 1 .

 1 Fig. 1. Iterative soft MPIC structure

Fig. 2 .

 2 Fig. 2. Iterative soft MPIC combined with HARQ

Fig. 3 .

 3 Fig. 3. The profile of ITU-Pedestrian B channel

Fig. 4 .

 4 Fig.[START_REF] Miki | Multipath Interference Canceller Using Soft-decision Replica Combined with Hybrid ARQ in W-CDMA Forward Link[END_REF]. BLER for CQI 16 in ITU-Pedestrian B channel observe a performance floor which limited the BLER to a value above 10 -1 . With the MPIC and other techniques, the floor is completely removed. In case of CQI 22, the MPIC curve presents a performance floor due to the sever multipath interference caused by the ITU-Pedestrian B channel. This floor is suppressed when using HARQ. The use of CoRe with CoRe resulted in great performance enhancement over the MPIC. This is due to the diversities offered by Chase combining and CoRe.BLER results for CQI 23 and CQI 24 are shown in Fig.7and Fig.8respectively. We see that for CQI 23 the error floor observed in MPIC curve, is removed by HARQ, whereas the HARQ curve for CQI 24 have a lowered floor compared

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig. 5. BLER for CQI 18 in ITU-Pedestrian B channel

Fig. 8 .Fig. 9 .

 89 Fig. 8. BLER for CQI 24 in ITU-Pedestrian B channel

Fig. 10 .

 10 Fig. 10. Throughput for CQI 18 in ITU-Pedestrian B channel

  Simulations were done for several channel quality indicator (CQI) modes to observe the performance enhancement. The predifined CQI modes used in this work are shown in TABLEIII[START_REF] Gpp | Spreading and modulation (FDD)[END_REF]. Performance is presented in terms of the residual BLER

	V. SIMULATION RESULTS
	TABLE II
	SIMULATION PARAMETERS
	Chip rate	3.84 Mcps
	Symbol rate	240 Ksps
	Spreading Factor (SF)	16
	Transmission Time Interval (TTI)	2 ms
	Power Control	None
	Spreading code	OVSF codes
	Modulation	16-QAM
	Channel coding/decoding	Turbo coding / Max-Log-MAP
	Number of turbo decoding iterations	10
	HARQ	Chase with CoRe
	CRC	24 bits
	Max. Number of HARQ processes	2
	Delay between HARQ retransmissions (RTD)	12 ms


Vincent Lostanlen
email: vincent.lostanlen@nyu.edu

Joakim Andén
email: janden@flatironinstitute.edu

Mathieu Lagrange
email: mathieu.lagrange@cnrs.fr

Extended playing techniques: The next milestone in musical instrument recognition

Keywords: Information systems → Music retrieval, Multimedia databases, Nearest-neighbor search, • Applied computing → Sound and music computing, playing technique similarity, musical instrument recognition, scattering transform, metric learning, large-margin nearest neighbors

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.

INTRODUCTION

The gradual diversification of the timbral palette in Western classical music since the dawn of the 20th century is reflected in five concurrent trends: the addition of new instruments to the symphonic instrumentarium, either by technological inventions (e.g. theremin) or importation from non-Western musical cultures (e.g. marimba) [53, epilogue]; the creation of novel instrumental associations, as epitomized by Klangfarbenmelodie [54, chapter 22]; the temporary alteration of resonant properties through mutes and other "preparations" [START_REF] Dianova | John Cage's Prepared Piano: The Nuts and Bolts[END_REF]; a more systematic usage of extended instrumental techniques, such as artificial harmonics, col legno batutto, or flutter tonguing [32, chapter 11]; and the resort to electronics and digital audio effects [START_REF] Zölzer | DAFX: Digital Audio Effects[END_REF]. The first of these trends has somewhat stalled. To this day, most Western composers rely on an acoustic instrumentarium that is only marginally different from the one that was available in the Late Romantic period. Nevertheless, the remaining trends in timbral diversification have been adopted on a massive scale in post-war contemporary music. In particular, an increased concern for the concept of musical gesture [START_REF] Inge | Musical Gestures: Sound, Movement, and Meaning[END_REF] has liberated many unconventional instrumental techniques from their figurativistic connotations, thus making the so-called "ordinary" playing style merely one of many compositional -and improvisational -options.

Far from being exclusive to contemporary music, extended playing techniques are also commonly found in oral tradition; in some cases, they even stand out as a distinctive component of musical style. Four well-known examples are the snap pizzicato ("slap") of the upright bass in rockabilly, the growl of the tenor saxophone in rock'n'roll, the shuffle stroke of the violin ("fiddle") in Irish folklore, and the glissando of the clarinet in Klezmer music. Consequently, the organology (the instrumental what?) of a recording, as opposed to its chironomics (the gestural how?), is a poor organizing principle for browsing and recommendation in large music databases.

Yet, past research in music information retrieval (MIR), and especially in machine listening, rarely acknowledges the benefits of integrating the influence of performer gesture into a coherent taxonomy of musical instrument sounds. Instead, gesture is often framed as a spurious form of intra-class variability between instruments without delving into its interdependencies with pitch and intensity. In other works, it is conversely used as a probe for the acoustical study of a given instrument without emphasis on the broader picture of orchestral diversity.

One major cause of this gap in research is the difficulty of collecting and annotating data for contemporary instrumental techniques. Fortunately, this obstacle has recently been overcome, owing to the creation of databases of instrumental samples for music orchestration in spectral music [START_REF] Maresz | On computer-assisted orchestration[END_REF]. In this work, we capitalize on the availability of this data to formulate a new line of research in MIR, namely the joint retrieval of organological ("what instrument is being played in this recording?") and chironomical information ("how is the musician producing sound?"), while remaining invariant to other factors of variability deliberately regarded as contextual. These include at what pitch and intensity the music was recorded, but also where, when, why, by whom, and for whom it was created.

Figure 1a shows the constant-Q wavelet scalogram (i.e. the complex modulus of the constant-Q wavelet transform) of a trumpet musical note, as played with an ordinary technique. Unlike most existing publications on instrument classification (e.g. 1a vs. 1b), which exclusively focus on intra-class variability due to pitch (Figure 1c) and intensity (Figure 1d), and mute (1e), this work aims to also account for the presence of instrumental playing techniques (IPTs), such as changes in tone quality (Figure 1f), attack (Figure 1g), tonguing (Figure 1h), and articulation (Figure 1i). These factors are considered either as intra-class variability, for the instrument recognition task, or as inter-class variability, for the IPT recognition task. The analysis of IPTs whose definition involves more than a single musical event, such as phrasing (Figure 1j), is beyond the scope of this paper.

Section 2 reviews the existing literature on the topic. Section 3 defines taxonomies of instruments and gestures from which the IPT classification task is derived. Section 4 describes how two topics in machine listening, namely characterization of amplitude modulation and incorporation of supervised metric learning, are relevant to address this task. Section 5 reports the results from an IPT classification benchmark on the Studio On Line (SOL) dataset.

RELATED WORK

This section reviews recent MIR literature on the audio analysis of IPTs with a focus on the datasets available for the various classification tasks considered.

Isolated note instrument classification

The earliest works on musical instrument recognition restricted their scope to individual notes played with an ordinary technique, eliminating most factors of intra-class variability due to the performer [START_REF] Benetos | Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection[END_REF][START_REF] Brown | Computer identification of musical instruments using pattern recognition with cepstral coefficients as features[END_REF][START_REF] Eronen | Musical instrument recognition using cepstral coefficients and temporal features[END_REF][START_REF] Herrera Boyer | Automatic classification of musical instrument sounds[END_REF][START_REF] Kaminskyj | Automatic recognition of isolated monophonic musical instrument sounds using kNNC[END_REF][START_REF] Martin | Musical instrument identification: A pattern recognition approach[END_REF][START_REF] Wieczorkowska | Analysis of feature dependencies in sound description[END_REF]. These results were obtained on datasets such as MUMS [50], MIS,1 RWC [START_REF] Goto | RWC music database: music genre database and musical instrument sound database[END_REF], and samples from the Philharmonia Orchestra. 2 This line of work culminated with the development of a support vector machine classifier trained on spectrotemporal receptive fields (STRF), which are idealized computational models of neurophysiological responses in the central auditory system [START_REF] Chi | Multiresolution spectrotemporal analysis of complex sounds[END_REF]. Not only did this classifier attain a near-perfect mean accuracy of 98.7% on the RWC dataset, but the confusion matrix of its predictions was close to that human listeners [START_REF] Patil | Music in our ears: the biological bases of musical timbre perception[END_REF].

Therefore, supervised classification of instruments from recordings of ordinary notes could arguably be considered a solved problem; we refer to [START_REF] Bhalke | Automatic musical instrument classification using fractional Fourier transform based-MFCC features and counter propagation neural network[END_REF] for a recent review of the state of the art.

Solo instrument classification

A straightforward extension of the problem above is the classification of solo phrases, encompassing some variability in melody [START_REF] Krishna | Music instrument recognition: from isolated notes to solo phrases[END_REF], for which the accuracy of STRF models is around 80% [START_REF] Patil | Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases[END_REF]. Since the Western tradition of solo music is essentially limited to a narrow range of instruments (e.g. piano, classical guitar, violin) and genres (sonatas, contemporary, free jazz, folk), datasets of solo phrases, such as solosDb [START_REF] Joder | Temporal integration for audio classification with application to musical instrument classification[END_REF], are exposed to strong biases. This issue is partially mitigated by the recent surge of multitrack datasets, such as MedleyDB [START_REF] Bittner | MedleyDB: A multitrack dataset for annotationintensive MIR research[END_REF], which has spurred a renewed interest in single-label instrument classification [START_REF] Yip | An accurate open-source solo musical instrument classifier[END_REF]. In addition, the cross-collection evaluation methodology [START_REF] Livshin | The importance of cross database evaluation in sound classification[END_REF] reduces the risk of overfitting caused by the relative homogeneity of artists and recording conditions in these small datasets [START_REF] Bogdanov | Cross-collection evaluation for music classification tasks[END_REF]. To date, the best classifiers of solo recordings are the joint time-frequency scattering transform [START_REF] Andén | Classification with Joint Time-Frequency Scattering[END_REF] and the spiral convolutional network [START_REF] Lostanlen | Deep convolutional networks on the pitch spiral for musical instrument recognition[END_REF] trained on the Medley-solos-DB dataset [START_REF] Vincent Lostanlen | Medley-solos-DB: a cross-collection dataset of solo musical phrases[END_REF], i.e., a cross-collection dataset which aggregates MedleyDB and solosDb following the procedure of [START_REF] Donnelly | Cross-Dataset Validation of Feature Sets in Musical Instrument Classification[END_REF]. We refer to [START_REF] Han | Deep convolutional neural networks for predominant instrument recognition in polyphonic music[END_REF] for a recent review of the state of the art.

Multilabel classification in polyphonic mixtures

Because most publicly released musical recordings are polyphonic, the generic formulation of instrument recognition as a multilabel classification task is the most relevant for many end-user applications [START_REF] José Burred | Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope[END_REF][START_REF] Luis | Polyphonic instrument recognition using spectral clustering[END_REF]. However, it suffers from two methodological caveats. First, polyphonic instrumentation is not independent from other attributes, such as geographical origin, genre, or key. Second, the inter-rater agreement decreases with the number of overlapping sources [22, chapter 6]. These problems are all the more troublesome since there is currently no annotated dataset of polyphonic recordings diverse enough to be devoid of artist bias. The Open-MIC initiative, from the newly created Community for Open and Sustainable Music and Information Research (COSMIR), is working to mitigate these issues in the near future [START_REF] Mcfee | A plan for sustainable MIR evaluation[END_REF]. We refer to [START_REF] Humphrey | OpenMIC-2018: an open dataset for multiple instrument recognition[END_REF] for a recent review of the state of the art.

Solo playing technique classification

Finally, there is a growing interest for studying the role of the performer in musical acoustics, from the perspective of both sound production and perception. Apart from its interest in audio signal processing, this topic is connected to other disciplines, such as biomechanics and gestural interfaces [START_REF] Metcalf | Complex hand dexterity: a review of biomechanical methods for measuring musical performance[END_REF]. The majority of the literature focuses on the range of IPTs afforded by a single instrument.

Recent examples include clarinet [START_REF] Loureiro | Timbre Classification Of A Single Musical Instrument[END_REF], percussion [START_REF] Tindale | Retrieval of percussion gestures using timbre classification techniques[END_REF], piano [START_REF] Bernays | Expressive production of piano timbre: touch and playing techniques for timbre control in piano performance[END_REF], guitar [START_REF] Chen | Electric Guitar Playing Technique Detection in Real-World Recording Based on F0 Sequence Pattern Recognition[END_REF][START_REF] Raphael Foulon | Automatic classification of guitar playing modes[END_REF][START_REF] Su | Sparse Cepstral, Phase Codes for Guitar Playing Technique Classification[END_REF], violin [START_REF] Young | Classification of Common Violin Bowing Techniques Using Gesture Data from a Playable Measurement System[END_REF], and erhu [START_REF] Yang | Cross-cultural Comparisons of Expressivity in Recorded Erhu and Violin Music: Performer Vibrato Styles[END_REF]. Some publications frame timbral similarity in a polyphonic setting, yet do so according to a purely perceptual definition of timbre -with continuous attributes such as brightness, warmth, dullness, roughness, and so forth -without connecting these attributes to the discrete latent space of IPTs (i.e., through a finite set of instructions, readily interpretable by the performer) [START_REF] Antoine | Musical Acoustics, Timbre, and Computer-Aided Orchestration Challenges[END_REF]. We refer to [START_REF] Leman | On the Role of the Hand in the Expression of Music[END_REF] for a recent review of the state of the art.

In the following, we define the task of retrieving musical timbre parameters across a range of instruments found in the symphonic orchestra. These parameters are explicitly defined in terms of sound production rather than by means of perceptual definitions.

TASKS

In this section, we define a taxonomy of musical instruments and another for musical gestures, which are then used for defining the instrument and IPT query-by-example tasks. We also describe the dataset of instrument samples used in our benchmark.

Taxonomies

The Hornbostel-Sachs taxonomy (H-S) organizes musical instruments only according to their physical characteristics and purposefully ignores sociohistorical background [START_REF] Montagu | It's time to look at Hornbostel-Sachs again[END_REF]. Since it offers an unequivocal way of describing any acoustic instrument without any prior knowledge of its applicable IPTs, it serves as a lingua franca in ethnomusicology and museology, especially for ancient or rare instruments which may lack available informants. The classification of the violin in H-S (321.322-71), as depicted in Figure 2, additionally encompasses the viola and the cello. The reason is that these three instruments possess a common morphology. Indeed, both violin and viola are usually played under the jaw and the cello is held between the knees, these differences in performer posture are ignored by the H-S classification. Accounting for these differences begs to refine H-S by means a vernacular taxonomy. Most instrument taxonomies in music signal processing, including MedleyDB [START_REF] Bittner | MedleyDB: A multitrack dataset for annotationintensive MIR research[END_REF] and AudioSet [START_REF] Jort | Audio Set: An ontology and human-labeled dataset for audio events[END_REF], adopt the vernacular level rather than conflating all instruments belonging to the same H-S class. A further refinement includes potential alterations to the manufactured instrument -permanent or temporary, at the time scale one or several notes -that affect its resonant properties, e.g., mutes and other preparations [START_REF] Dianova | John Cage's Prepared Piano: The Nuts and Bolts[END_REF]. The only node in the MedleyDB taxonomy which reaches this level of granularity is tack piano [START_REF] Bittner | MedleyDB: A multitrack dataset for annotationintensive MIR research[END_REF] . In this work, we will not consider variability due to the presence of mutes as discriminative, both for musical instruments and IPTs.

Unlike musical instruments, which are amenable to a hierarchical taxonomy of resonating objects, IPTs result from a complex synchronization between multiple gestures, potentially involving both hands, arms, diaphragm, vocal tract, and sometimes the whole body. As a result, they cannot be trivially incorporated into H-S, or indeed any tree-like structure [START_REF] Kolozali | Knowledge Representation Issues in Musical Instrument Ontology Design[END_REF]. Instead, an IPT is described by a finite collection of categories, each belonging to a different "namespace." Figure 3 illustrates such namespaces for the case of the violin. It therefore appears that, rather than aiming for a mere increase in granularity with respect to H-S, a coherent research program around extended playing techniques should formulate them as belonging to a meronomy, i.e., a modular entanglement of part-whole relationships, in the fashion of the Visipedia initiative in computer vision [START_REF] Belongie | Visipedia circa 2015[END_REF]. In recent years, some works have attempted to lay the foundations of such a modular approach, with the aim of making H-S relevant to contemporary music creation [START_REF] Magnusson | Musical Organics: A Heterarchical Approach to Digital Organology[END_REF][START_REF] Weisser | Rethinking musical instrument classification: towards a modular approach to the Hornbostel-Sachs system[END_REF]. However, such considerations are still in large part speculative and offer no definitive procedure for evaluating, let alone training, information retrieval systems.

Application setting and evaluation

In what follows, we adopt a middle ground position between the two aforementioned approaches: neither a top-down multistage classifier (as in a hierarchical taxonomy), nor a caption generator (as in a meronomy), our system is a query-by-example search engine in a large database of isolated notes. Given a query recording x(t), such a system retrieves a small number k of recordings judged similar to the query. In our system, we implement this using a knearest neighbors (k-NN) algorithm. The nearest neighbor search is not performed in the raw waveform domain of x(t), but in a feature space of translation-invariant, spectrotemporal descriptors. In what follows, we use mel-frequency cepstral coefficients (MFCCs) as a baseline, which we extend using second-order scattering coefficients [START_REF] Andén | Deep scattering spectrum[END_REF][START_REF] Mallat | Group invariant scattering[END_REF]. All features over averaged over the entire recording to create single feature vector. The baseline k-NN algorithm is applied using the standard Euclidean distance in feature space. To improve performance, we also apply it using a weighted Euclidean distance with a learned weight matrix.

In the context of music creation, the query x(t) may be an instrumental or vocal sketch, a sound event recorded from the environment, a computer-generated waveform, or any mixture of the above [START_REF] Maresz | On computer-assisted orchestration[END_REF]. Upon inspecting the recordings returned by the search engine, the composer may decide to retain one of the retrieved notes. Its attributes (pitch, intensity, and playing technique) are then readily available for inclusion in the musical score.

Faithfully evaluating such a system is a difficult procedure, and ultimately depends on its practical usability as judged by the composer. Nevertheless, a useful quantitative metric for this task is the precision at k (P@k) of the test set with respect to the training set, either under an instrument taxonomy and an IPT taxonomy. This metric is defined as the proportion of "correct" recordings returned for a given query, averaged over all queries in the test set. For our purposes, a returned recording is correct if it is of the same class as the query for a specific taxonomy. In all subsequent experiments, we report P@k for the number of retrieved items k = 5.

Studio On Line dataset (SOL)

The Studio On Line dataset (SOL) was recorded at IRCAM in 2002 and is freely downloadable as part of the Orchids software for computer-assisted orchestration. 3 It comprises 16 musical instruments playing 25444 isolated notes in total. The distribution of these notes, shown in Figure 4, spans the full combinatorial diversity of intensities, pitches, preparations (i.e., mutes), and all applicable playing techniques. The distribution of playing techniques is unbalanced as seen in Figure 5. This is because some playing techniques are shared between many instruments (e.g., tremolo) whereas other are instrument-specific (e.g., xylophonic, which is specific to the harp). The SOL dataset has 143 IPTs in total, and 469 applicable instrument-mute-technique triplets. As such, the dataset has considerable intra-class variability under both the instrument and IPTs taxonomies.

METHODS

In this section, we describe the scattering transform used to capture amplitude modulation structure and supervised metric learning which constructs a similarity measure suited for our query-byexample task.

Scattering transform

The scattering transform is a cascade of constant-Q wavelet transforms alternated with modulus operators [START_REF] Andén | Deep scattering spectrum[END_REF][START_REF] Mallat | Group invariant scattering[END_REF]. Given a signal x(t), its first layer outputs the first-order scattering coefficients 3 http://forumnet.ircam.fr/product/orchids-en/ S 1 x(λ 1 , t), which captures the intensity of x(t) at frequency λ 1 . Its frequency resolution is logarithmic in λ 1 and is sampled using Q 1 = 12 bins per octave. The second layer of the cascade yields the second-order scattering coefficients S 2 x(λ 1 , λ 2 , t), which extract amplitude modulation at frequency λ 2 in the subband of x(t) at frequency λ 1 . Both first-and second-order coefficients are averaged in time over the whole signal. The modulation frequencies λ 2 are logarithmically spaced with Q 2 = 1 bin per octave. In the following, we denote by Sx(λ, t) the concatenation of all scattering coefficients, where λ corresponds to either a single λ 1 for first-order coefficients or a pair (λ 1 , λ 2 ) for second-order coefficients.

The first-order scattering coefficients are equivalent to the melfrequency spectrogram which forms a basis for MFCCs [START_REF] Andén | Deep scattering spectrum[END_REF]. Secondorder coefficients, on the other hand, characterize common nonstationary structures in sound production, such as tremolo, vibrato, and dissonance [2, section 4]. As a result, these coefficients are better suited to model extended IPTs. We refer to [START_REF] Andén | Deep scattering spectrum[END_REF] an introduction on scattering transforms for audio signals and to [36, sections 3.2 and 4.5] for a discussion on its application to musical instrument classification in solo recordings and its connections to STRFs.

To match a decibel-like perception of loudness, we apply the adaptive, quasi-logarithmic compression

Sx i (λ, t) = log 1 + Sx i (λ, t) ε × µ(λ) (1)
where ε = 10 -3 and µ(λ) is the median of Sx i (λ, t) across t and i.

Metric learning

Linear metric learning algorithms construct a matrix L such that the weighted distance

D L (x i , x j ) = ∥L( Sx i -Sx j )∥ 2 (2)
between all pairs of samples (x i , x j ) optimizes some objective function. We refer to [START_REF] Bellet | A survey on metric learning for feature vectors and structured data[END_REF] for a review of the state of the art. In the following, we shall consider the large-margin nearest neighbors (LMNN) algorithm. It attempts to construct L such that for every signal x i (t) the distance D L (x i , x j ) to x j (t), one of its k nearest neighbors, is small if x i (t) and x j (t) belong to the same class and large otherwise. The matrix L is obtained by applying the specialpurpose solver of [58, appendix A]. In subsequent experiments, disabling LMNN is equivalent to setting L to the identity matrix, which yields the standard Euclidean distance on the scattering coefficients Sx(λ, t).

Compared to a class-wise generative model, such a Gaussian mixture model, a global linear model ensures some robustness to minor alterations of the taxonomy. Indeed, the same learned metric can be applied to similarity measures in related taxonomies without retraining. This stability is important in the context of IPT, where one performer's slide is another's glissando. A major drawback of LMNN is its dependency on the standard Euclidean distance for determining nearest neighbors [START_REF] Mcfee | Metric learning to rank[END_REF]. However, this is alleviated for scattering coefficients, since the scattering transform Sx(t, λ) is Lipschitz continuous to elastic deformation in the signal x(t) [START_REF] Mallat | Group invariant scattering[END_REF]Theorem 2.16]. In other words, the Euclidean distance between the scattering transform of x(t) and a deformed version of the same signal is bounded by the extent of that deformation.

EXPERIMENTAL EVALUATION

In this section, we study a query-by-example browsing system for the SOL dataset based on nearest neighbors. We discuss how the performance of the system is affected by the choice of feature (MFCCs or scattering transforms) and distance (Euclidean or LMNN), both quantitatively and qualitatively. Finally, we visualize the two feature spaces using nonlinear dimensionality reduction.

Instrument recognition

In the task of instrument recognition, we provide a query x(t) and the system retrieves k recordings x 1 (t), . . . , x k (t). We consider a retrieved recording to be relevant to the query if it corresponds to the same instrument, regardless of pitch, intensity, mute, and IPT. We therefore apply the LMNN with instruments as class labels. This lets us compute the precision at rank 5 (P@5) for a system by counting the number of relevant recordings for each query.

We compare scattering features to a baseline of MFCCs, defined as the 13 lowest coefficients of the discrete cosine transform (DCT) applied to the logarithm of the 40-band mel-frequency spectrum. For the scattering transform, we vary the maximum time scale T of amplitude modulation from 25 ms to 1 s. In the case of the MFCCs, T = 25 ms corresponds to the inverse of the lowest audible frequency (T -1 = 40 Hz). Therefore, increasing the frame duration beyond this scale has little effect since no useful frequency information would be obtained.

The left column of Figure 6 summarizes our results. MFCCs reach a relatively high P@5 of 89%. Keeping all 40 DCT coefficients rather than the lowest 13 brings P@5 down to 84%, because the DCT coefficients are most affected by spurious factors of intra-class variability, such as pitch and spectral flatness [36, subsection 2.3.3].

At the smallest time scale T = 25 ms, the scattering transform reaches a P@5 of 89%, thus matching the performance of the MFCCs. This is expected since there is little amplitude modulation below this scale, corresponding to λ 2 over 40 Hz, so the scattering transform is dominated by the first order, which is equivalent to MFCCs [START_REF] Andén | Deep scattering spectrum[END_REF]. Moreover, disabling median renormalization degrades P@5 down to 84%, while disabling logarithmic compression altogether degrades it to 76%. This is consistent with [START_REF] Vincent Lostanlen | Relevance-based Quantization of Scattering Features for Unsupervised Mining of Environmental Audio[END_REF], which applies scattering transform to a query-by-example retrieval task for acoustic scenes.

On one hand, replacing the canonical Euclidean distance by a distance learned by LMNN marginally improves P@5 for the MFCC baseline, from 89.3% to 90.0%. Applying LMNN to scattering features, on the other hand, significantly improves their performance with respect to the Euclidean distance, from 89.1% to 98.0%.

The dimensionality of scattering coefficients is significantly higher than that of MFCCs, which only consists of 13 coefficients. A concern is therefore that the higher dimensionality of the scattering coefficients may result in overfitting of the metric learning algorithm, artificially inflating its performance. To address this, we supplement the averaged MFCCs by higher-order summary statistics. In addition the 13 average coefficients, we also compute the average of all polynomial combinations of degree less than three. The resulting vector is of dimension 494, comparable to the that of the scattering vector. This achieves a P@5 of 91%, that is, slightly above the baseline. The increased performance of the scattering transform is therefore not likely due overfitting but to its better characterization of multiresolution structure.

Finally, increasing T from 25 ms up to 1 s -i.e., including all amplitude modulations between 1 Hz and 40 Hz -brings LMNN to a near-perfect P@5 of 99.7%. Not only does this result confirm that straightforward techniques in audio signal processing (here, wavelet scattering and metric learning) are sufficient to retrieve the instrument from a single ordinary note, it also demonstrates that the results remain satisfactory despite large intra-class variability in terms of pitch, intensity, usage of mutes, and extended IPTs. In other words, the monophonic recognition of Western instruments is, all things considered, indeed a solved problem.

Playing technique recognition

The situation is different when considering IPT, rather than instrument, as the reference for evaluating the query-by-example system. In this setting, a retrieved item is considered relevant if and only if it shares the same IPT as the query, regardless of instrument, mute, pitch, or dynamics. Therefore, we apply the LMNN with IPTs instead of instruments as class labels, yielding a different distance function optimized to distinguish playing techniques. The right column if Figure 6 summarizes our results. The MFCC baseline has a low P@5 of 44.5%, indicating that its coarse description of the short-term spectral envelope is not sufficient to model acoustic similarity in IPT. Perhaps more surprisingly, we find that optimal performance is only achieved by combining all proposed improvements: logscattering coefficients with median renormalization, T = 500 ms, and LMNN. This yields a P@5 of 63.0%. Indeed, an ablation study of that system reveals that, all other things being equal, reducing T to 25 ms brings the P@5 to 53.3%, disabling LMNN reduces it to 50.0%, and replacing scattering coefficients by MFCCs yields 48.4%. This result contrasts with the instrument recognition setting: whereas the improvements brought by the three aforementioned modifications are approximately additive in P@5 for musical instruments, they interact in a super-additive manner for IPTs. In particular, it appears that increasing T above 25 ms is only beneficial to IPT similarity retrieval if combined with LMNN.

Qualitative error analysis

For demonstration purposes, we select an audio recording x(t) to query two versions of the proposed query-by-example system. The first version uses MFCCs with T = 25 ms and LMNN; it has a P@5 of 48.4% for IPT retrieval. The second version uses scattering coefficients with T = 1 s, logarithmic transformation with median renormalization (see Equation 1), and LMNN; it has a P@5 of 63.0% for IPT retrieval. Both versions adopt IPT labels as reference for training LMNN. The main difference between the two versions is the choice of spectrotemporal features.

Figure 7 shows the constant-Q scalograms of the five retrieved items for both versions of the system as queried by the same audio signal x(t): a violin note from the SOL dataset, played with ordinary playing technique on the G string with pitch G4 and mf dynamics. Both versions correctly retrieve five violin notes which vary from the query in pitch, dynamics, string, and use of mute. Therefore, both systems have an instrument retrieval P@5 of 100% for this query. However, although the scattering-based version is also 100% correct in terms of IPT retrieval (i.e., it retrieves five ordinario notes), the MFCC-based version is only 40% correct. Indeed, three recordings exhibit on of the tremolo or sul ponticello playing techniques. We hypothesize that the confusion between ordinario and tremolo is caused by the presence of vibrato in the ordinary query since MFCCs cannot distinguish amplitude modulations (tremolo) from frequency modulations (vibrato) for the same modulation frequency [START_REF] Andén | Scattering representation of modulated sounds[END_REF]. These differences, however, are perceptually small and in some musical contexts vibrato and tremolo are used interchangeably.

The situation is different when querying both systems with recording x(t) exhibiting an extended rather than ordinary IPT. In contrast, four out of the five items retrieved by the MFCC system have an ordinario IPT instead of flatterzunge. This shortcoming has direct implications on the usability of the MFCC query-byexample system for contemporary music creation. More generally, this system is less reliable when queried with extended IPTs.

Unlike instrument similarity, IPT similarity seems to depend on long-range temporal dependencies in the audio signal. In addition, it is not enough to capture the raw amplitude modulation provided by the second-order scattering coefficients. Instead, an adaptive layer on top of this is needed to extract the discriminative elements from those coefficients. Here, that layer consists of the LMNN metric learning algorithm, but other methods may work equally well.

Feature space visualization

To visualize the feature space generated by MFCCs and scattering transforms, we embed them using diffusion maps. These embeddings preserve local distances while reducing dimensionality by forming a graph from those distances and calculating the eigenvectors of its graph Laplacian [START_REF] Coifman | Diffusion maps[END_REF]. Diffusion maps have previously been used to successfully visualize scattering coefficients [START_REF] Chudácek | Low dimensional manifold embedding for scattering coefficients of intrapartum fetal heart rate variability[END_REF][START_REF] Villoutreix | Synthesizing developmental trajectories[END_REF].

Figure 9 shows embeddings of MFCCs and scattering coefficients, both post-processed using LMNN, for different subsets of recordings. In Figure 9a, we see how the MFCCs fail to separate violin and trumpet notes for the ordinario playing technique. Scattering coefficients, on the other hand, successfully separate the instruments as seen in Figure 9b. Similarly, Figures 9c and9d show how, restricted to bowed instruments (violin, viola, violoncello, and contrabass), MFCCs do not separate the ordinario from tremolo playing techniques, while scattering coefficients discriminates well. These visualizations provide motivation for our choice of scattering coefficients to represent single notes.

CONCLUSION

Whereas the MIR literature abounds on the topic of musical instrument recognition for so-called "ordinary" isolated notes and solo performances, little is known about the problem of retrieving the instrumental playing technique from an audio query within a fine-grained taxonomy. Yet the knowledge of IPT is a precious source of musical information, not only to characterize the physical interaction between player and instrument, but also in the realm of contemporary music creation. It also bears an interest for organizing digital libraries as a mid-level descriptor of musical style. To the best of our knowledge, this paper is the first to benchmark query-by-example MIR systems according to a large-vocabulary, multi-instrument IPT reference (143 classes) instead of an instrument reference. We find that this new task is considerably more challenging than musical instrument recognition as it amounts to characterizing spectrotemporal patterns at various scales and comparing them in a non-Euclidean way. Although the combination of methods presented here -wavelet scattering and large-margin nearest neighbors -outperforms the MFCC baseline, its accuracy on the SOL dataset certainly leaves room for future improvements. For example, we could replace the standard time scattering transform with joint time-frequency scattering transform [START_REF] Andén | Classification with Joint Time-Frequency Scattering[END_REF].

The evaluation methodology presented here uses ground truth IPT labels to quantify the relevance of returned items. This approach is useful in that the labels are unambiguous, but it might be too coarse to reflect practical use. Indeed, as it is often the case in MIR, some pairs of labels are subjectively more similar than others. For example, slide is evidently closer to glissando than to pizzicatobartok. The collection of subjective ratings for IPT similarity, and its comparison with automated ratings, is left as future work. Another promising avenue of research is to formulate a structured prediction task for isolated musical notes, simultaneously estimating the pitch, dynamics, instrument, and IPT to construct a unified machine listening system, akin to a caption generator in computer vision. fruitful discussions on Visipedia, and Katherine Crocker for a helpful suggestion on the title of this article. This work is supported by the ERC InvariantClass grant 320959.

t λ 1 (

 1 a) Trumpet note (ordinario).

  Tonguing (flatterzunge).

t λ 1 (

 1 i) Articulation (trill).

t λ 1 (

 1 j) Phrasing (détaché).

Figure 1 :

 1 Figure 1: Ten factors of variations of a musical note: pitch (1c), intensity (1d), tone quality (1f), attack (1g), tonguing (1h), articulation (1i), mute (1e), phrasing (1j), and instrument (1b).

Figure 2 :Figure 3 :

 23 Figure 2: Taxonomy of musical instruments.

Figure 4 :

 4 Figure 4: Instruments in the SOL dataset.

Figure 5 :

 5 Figure 5: The 50 most common IPTs in the SOL dataset.

Figure 6 :

 6 Figure 6: Summary of results on the SOL dataset.

  Figure 8 is analogous to Figure 7 but with a different audio query. The query is a trumpet note from the SOL dataset, played with the flatterzunge (flutter-tonguing) technique, pitch G4, and mf dynamics. Again, the scattering-based version retrieves five recordings with the same instrument (trumpet) and IPT (flatterzunge) as the query.

t λ 1

 1 Query: Violin, ordinario, G4, mf, on G string.

5 :

 5 tremolo, C♯5, pp.

Figure 7 :

 7 Figure 7: Five nearest neighbors of the same query (a violin note with ordinary playing technique, at pitch G4, mf dynamics, played on the G string), as retrieved by two different versions of our system: with MFCC features (left) and with scattering transform features (right). The captions denote the musical attribute(s) that differ from those of the query: mute, playing technique, pitch, and dynamics.

t λ 1

 1 Query: Trumpet in C, flatterzunge, G4, mf.

t λ 1 2:

 1 ordinario, F4.

Figure 8 :

 8 Figure 8: Five nearest neighbors of the same query (a trumpet note with flatterzunge technique, at pitch G4, mf dynamics), as retrieved by two different versions of our system: with MFCC features (left) and with scattering transform features (right).The captions of each subfigure denotes the musical attribute(s) that differ from those of the query.

  (a) Instrument embedding with MFCC. (b) Instrument embedding with scattering transform. (c) Playing technique embedding with MFCC. (d) Playing technique embedding with scattering transform.

Figure 9 :

 9 Figure 9: Diffusion maps produce low-dimensional embeddings of MFCC features (left) vs. scattering transform features (right).In the two top plots, each dot represents a different musical note, after restricting the SOL dataset to the ordinario playing technique of each of the 31 different instrument-mute couples. Blue (resp. orange) dots denote violin (resp. trumpet in C) notes, including notes played with a mute: sordina and sordina piombo (resp. cup, harmon, straight, and wah). In the two bottom plots, each dot corresponds to a different musical note, after restricting the SOL dataset to 4 bowed instruments (violin, viola, violoncello, and contrabass), and keeping all 38 applicable techniques. Blue (resp. orange) dots denote tremolo (resp. ordinary) notes. In both experiments, the time scales of both MFCC and scattering transform are set equal to T = 1 s, and features are post-processed by means of the large-margin nearest neighbor (LMNN) metric learning algorithm, using playing technique labels as reference for reducing intra-class neighboring distances.

http://theremin.music.uiowa.edu/MIS.html

http://www.philharmonia.co.uk/explore/sound_samples

ACKNOWLEDGMENTS

The authors wish to thank Philippe Brandeis, Étienne Graindorge, Stéphane Mallat, Adrien Mamou-Mani, and Yan Maresz for contributing to the TICEL research project ("Traité instrumental collaboratif en ligne"), Andrew Farnsworth and Grant Van Horn for


Loïck Le Dantec
email: loick.le-dantec@inra.fr

Nabil Girollet

Mathieu Fouché

Jérôme Gouzy

Erika Sallet

Jose Quero-Garcia

Teresa Barreneche

Bénédicte Wenden

Elisabeth Dirlewanger

A High Quality Draft Assembly of the Diploid 'Regina' Sweet Cherry Genome

Sweet cherry (Prunus avium L.) is a diploid species with an estimated genome size of 338 MB (Arumuganathan et al. 1991). It is mostly self-incompatible, and therefore has a heterozygous genetic background. We have sequenced and assembled the genome of the 'Regina' sweet cherry variety, which is a late blooming cultivar. Here we present a draft phased sweet cherry genome assembly using a combination of sequencing strategies (long reads sequencing with PacBio RSII and optical mapping with BioNano whole-genome maps).

CONCLUSION

. PacBio long reads were de novo assembled using FALCON assembler and phased using FALCON UNZIP. BioNano optical whole genome maps were used for further scaffolding. Our de novo genome assembly resulted in a genome of 279 Mb (83 % of estimated genome size), 92 scaffolds and a largest scaffold of 16,3 Mb. The assembly has high contiguity (contig N50 1,23Mb, scaffold N50 ~6Mb) and good completeness with 95,9% BUSCO genes complete (scaffolds+ unscaffolded contigs). We are now in the process of reconstructing the pseudo molecules using high density genetic linkage maps and GBS data. Structural annotation is on the way using the integrative EuGene platform with different sources of evidence available for gene prediction including transcriptomic data from Regina obtained previously in the lab (Unigene set and RNASeq).

Optical Mapping Bionano

Plant and Animal Genome Conference January 13-17, 2018

178 Hybrid Assembly: Scaffolding

	PacBio Sequencing
	RSII P6-C4 chemistry		PacBio Assembly
	30 SMRT cells		Falcon and FalconUnzip
	Number of bases N50	Total (30SMRT Cells) 27,9 billions 17,5kb	# contigs	PacBio assembly Falcon (draft) 2019	PacBio assembly FalconUnzip (polished primary contigs) 1078	PacBio assembly FalconUnzip (polished haplotigs) 1289	Nicking enzyme Theoretical nb labels/100kb Real nb labels/100kb Filtered	BssS1 13,6 11 156(459X)
	Depth	82,5 x	N50 (Mb) L50 Max Size (Mb)	1,2 77 10,7	1,23 68 10,5	0,35 150 1,83	data>100kb (Gb) Molecules N50 (kb)
			total Size (Mb)	339	323	177		Bionano Assembly
			% genome				# contigs	433
			coverage (338 Mb)	100 %	95 %	52 %	Median length (Mb)	0,809
							N50 (Mb)	1,7
							Max Size (Mb)	9,9
							total Size (Mb)	535
							% genome
							coverage	158
							(338 Mb)
						PacBio polished
						primary contigs +
						bionano
						assembly
					# scaffolds	92
					Median length (Mb)	1,9
					N50 (Mb)	5,96
					Max Size (Mb)	16,3
					total Size (Mb)	279
					% genome
					coverage (

338 Mb) 83% Hybrid scaffolds + not scaffolded PacBio contigs : 338Mb Assessment of genome assembly : BUSCO (V2.0)

					Hybrid
				Hybrid	Assembly
	Contig set	PacBio FalconUnzip Primary + Haplotigs	Hybrid Assembly scaffolds	Assembly scaffolds + not scaffolded PacBio	scaffolds + not scaffolded PacBio Contigs + FalconUnzip
				Contigs	haplotigs
	Complete	95,6%	90,9%	93,6%	95,9%
	Duplicated	48,5%	2,9%	5%	46,5%
	Fragmented	1,3%	2,1%	1,7%	1,1%
	Missing	3,1%	7%	4,7%	3%

Acknowledgements

The sweet cherry genome sequencing was financed by the INRA 'Plant Biology and Breeding' division in the 'CherryGeno' project. The PacBio sequencing was done by the GeT-PlaGe INRA platform (http://get.genotoul.fr/) and the optical map was performed by INRA CNRGV (http://cnrgv.toulouse.inra.fr/). We are grateful to the genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul) for providing computing and storage resources.