N Etteyeb

L Dhouibi

H Takenouti

E Triki

Protection of reinforcement steel corrosion by phenyl phosphonic acid pre-treatment PART I: Tests in solutions simulating the electrolyte in the pores of fresh concrete

Keywords: Pre-treatment, Electrochemical impedance spectroscopy, Phenyl phosphonic Acid, Protective efficiency

The carbon steel electrodes were first treated by immersion in 0.1 M phenyl phosphonic acid (C 6 H 5 P(O)(OH) 2, PPA) solution during 24 or 72 hours, then they were transferred into the corrosion test solution representing interstice pore electrolyte polluted by seawater: sat. Ca(OH) 2 + 0.5M NaCl) to evaluate the protective effectiveness of the pre-treatment. The investigation was performed essentially by polarization curves and electrochemical impedance measurements with rotating disc electrode. Very high protective effect was observed with steel pre-treated during 72 hours with stationary electrode. From polarization and electrochemical impedance spectroscopy experiments, the protective efficiency was found to be in the 90-99% range for 72 hour pre-treatment. This protective property remains effective even one month immersion. EDS analysis showed the presence of the phosphorus on the steel surface, probably due to the presence of iron-PPA complex.

INTRODUCTION

Concrete usually provides an excellent corrosion protection for reinforcing steel because its high alkaline environment results in the formation of a tightly adhering passive film, which protects steel from corrosion. However, chloride ions are considered as the major cause to shorten the lifetime steel reinforcement by corrosion together with the carbonation process.

To minimize the corrosion processes, various procedures are proposed. Among them, the use of corrosion inhibitors was considered as a possible and cost effective solution [START_REF] Elsener | Migrating Corrosion Inhibitor Blend for Reinforced Concrete: Part 2-Inhibitor as Repair Strategy[END_REF][START_REF] Monticelli | A study on corrosion inhibitors for concrete application[END_REF][START_REF] Dhouibi | The application of electrochemical impedance spectroscopy to determine the long-term effectiveness of corrosion inhibitors for steel in concrete[END_REF][START_REF] Elsener | Corrosion inhibitors for reinforced concrete[END_REF][START_REF] Jamil | Electrochemical behaviour of amino alcohol-based inhibitors used to control corrosion of reinforcing steel[END_REF][START_REF] Batis | Corrosion protection investigation of reinforcement by inorganic coating in the presence of alkanolamine-based inhibitor[END_REF][START_REF] Berke | Predicting long-term durability of steel reinforced concrete with calcium nitrite corrosion inhibitor[END_REF].

Two approaches are generally used to evaluate the efficiency of inhibitors for concrete application; one uses simulated pore solution testing [START_REF] Monticelli | Corrosion inhibition of steel in alkaline chloride solutions[END_REF], and the other uses actual concrete or mortar specimens [START_REF] Tommaselli | Effectiveness of corrosion inhibitors in saturated calcium hydroxide solutions acidified by acid rain components[END_REF][START_REF] Monticelli | Influence of two specific inhibitors on steel corrosion in a synthetic solution simulating a carbonated concrete with chlorides[END_REF][START_REF] Zafeiropoulou | Performance evaluation of organic coatings against corrosion in reinforced cement mortars[END_REF]. Both methods are sometimes used in conjunction [START_REF] Mechmeche | Investigation of the early effectiveness of an amino-alcohol based corrosion inhibitor using simulated pore solutions and mortar specimens[END_REF].

Electrochemical techniques are actually used in both approaches. Simulated pore solutions usually employ a saturated calcium hydroxide solution [START_REF] Mechmeche | Investigation of the early effectiveness of an amino-alcohol based corrosion inhibitor using simulated pore solutions and mortar specimens[END_REF][START_REF] Goñi | Synthetic concrete pore solution chemistry and rebar corrosion rate in the presence of chlorides[END_REF][START_REF] Xu | Influence of N,N′-dimethylaminoethanol as an inhibitor on the chloride threshold level for corrosion of steel reinforcement[END_REF] with or without chloride addition. This methodology allows evaluating rapidly the inhibitor efficiency and determining the simplified system the inhibition mechanisms. However, further tests in mortar and concrete are needed because the possible interaction of the inhibitor with the solid phases of the cement paste would modify the concrete properties.

The molecules used to mitigate the corrosion of reinforcement steel contain generally phosphorus. They may be distinguished two groups; inorganic and organic.

Inorganic inhibitors, such as sodium phosphate (Na 3 PO 4 ) and sodium monofluorophosphate (Na 2 PO 3 F) were tested as corrosion inhibitors for steel reinforcement for concrete structure [START_REF] Dhouibi | Electrochemical methods for evaluating inhibitors of steel corrosion in concrete[END_REF][START_REF] Etteyeb | Corrosion inhibition of carbon steel in alkaline chloride media by Na 3 PO 4[END_REF][START_REF] Naceur | Electrochemical study of corrosion inhibition of steel reinforcement in alkaline solutions containing phosphates based components[END_REF][START_REF] Andrade | Preliminary testing of Na 2 PO 3 F as a curative corrosion inhibitor for steel reinforcements in concrete[END_REF][START_REF] Alonso | Na 2 PO 3 F as inhibitor of corroding reinforcement in carbonated concrete[END_REF][START_REF] Ngala | corrosion inhibitor systems for remedial treatment of reinforced concrete. Part 2: Sodium monofluorophosphate[END_REF]. Those previous works have demonstrated that sodium phosphate inhibits pitting corrosion of steel in pore solution if its concentration is equal or higher than the chloride concentration [START_REF] Dhouibi | Electrochemical methods for evaluating inhibitors of steel corrosion in concrete[END_REF]. However, its efficiency is partially lost in concrete. Sodium monofluorophosphate also prevents or decreases the rate of reinforcement corrosion in the presence of chloride [START_REF] Andrade | Preliminary testing of Na 2 PO 3 F as a curative corrosion inhibitor for steel reinforcements in concrete[END_REF], and in the presence of carbonation [START_REF] Alonso | Na 2 PO 3 F as inhibitor of corroding reinforcement in carbonated concrete[END_REF][START_REF] Ngala | corrosion inhibitor systems for remedial treatment of reinforced concrete. Part 2: Sodium monofluorophosphate[END_REF], but its efficiency depends on the method of application to concrete. Organic inhibitors such as nitrilotris(methylenephosphonic acid) was also tried as inhibitor and found to inhibit the cathodic reaction in a saturated Ca(OH) 2 solution [START_REF] Nakayama | Inhibitory effects of nitrilotris(methylenephosphonic acid) on cathodic reactions of steels in saturated Ca(OH) 2 solutions[END_REF].

Hydroxyethylidene-diphosphonic acid was also considered as corrosion inhibitor with nontoxicity in cooling water [START_REF] Garcia | Study of the scale inhibition by HEDP in a channel flow cell using a quartz crystal microbalance[END_REF][START_REF] Starostina | Inhibition by phosphates of mild steel and cast iron in cooling water[END_REF][START_REF] Cushner | Effect of iron(II) on the performance of calcium carbonate inhibitors = Influence du fer(II) sur les performances des inhibiteurs de carbonate de calcium[END_REF][START_REF] Sekine | Effect of 1-Hydroxyethylidene-1, 1-Diphosphonic Acid on the Corrosion of SS 41 Steel in 0.3% Sodium Chloride Solution[END_REF]. Phosphonic acids prevent the scale formation and corrosion process because of hydrolytic stability, ability to form complexes with iron [START_REF] Fang | Passive Films and Corrosion Protection Due to Phosphonic Acid Inhibitors[END_REF][START_REF] Rajendran | Corrosion inhibition by phenyl phosphonate and Zn[END_REF][START_REF] Gunasekaran | Synergistic inhibition in low chloride media[END_REF].

Rajendran et al. [START_REF] Rajendran | Corrosion inhibition by phenyl phosphonate and Zn[END_REF] have concluded that, in solution, both the phosphonate group and the phenyl group of the phenyl phosphonic acid (PPA) are involved in the formation of a complex with Zn 2+ and Fe 2+ on the one hand. On the other hand, in the solid state, only the phosphonate group of PPA is involved in the formation of a complex with Zn 2+ and Fe 2+ . To et al. [START_REF] To | A corrosion-protective film formed on a carbon steel by an organic phosphonate[END_REF] have tested three phosphonate compounds as corrosion inhibitors of carbon steel in chloride media. The investigation has demonstrated that Ethyllaurylphosphonate (ELP) formed a protective film. This film was relatively thick, porous, and was identified as the Fe(ELP) 2 complex.

It is also important to recall that the phosphate-based inhibitors are generally efficient for iron in alkaline medium, however these substances may combine with Ca 2+ of the concrete before reacting with steel, and its protective effect is strongly jeopardized. This is the reason that these substances may be used as pre-treatment of reinforcement steel before embedding into the concrete. Our previous investigation [START_REF] Etteyeb | Corrosion protection of steel reinforcement by a pretreatment in phosphate solutions: assessment of passivity by electrochemical techniques[END_REF] showed that the pre-treatment of steel rebars hinders the initiation of corrosion. The immersion of reinforcement rods during 7 days in 0.5M Na 3 PO 4 favours the formation of a passive layer on the steel surface, which is able to resist for longer period to the aggression of chlorides. Another study [START_REF] Etteyeb | Corrosion inhibition of carbon steel in alkaline chloride media by Na 3 PO 4[END_REF] demonstrated that the preventive method (pre-treatment) seems to be the better choice, since it leads to higher impedance values and consequently to lower corrosion rates. The impregnation duration of steel in 0.5M Na 3 PO 4 solution and the electrode rotation speed considerably influence the phosphate-based film resistance against corrosion.

The present paper deals with the protective effect of phenyl phosphonic acid (C 6 H 5 P(O)(OH) 2, PPA) used as pre-treatment solution against the corrosion reinforcement steel in a solution simulating interstitial electrolyte of concrete contaminated by seawater. The study was essentially performed by means of electrochemical techniques.

EXPERIMENTAL CONDITIONS

ELECTRODES AND ELECTROLYSING CELL

A classic electrochemical cell with three-electrode configuration was used in this study: a platinum grid, a rotating disk of steel, and a saturated calomel electrode respectively as counter, working, and reference electrodes.

The working electrode was made of cylinder rod of carbon steel (C ≈ 0.22 %) of 5 mm diameter. The lateral part was covered with a cataphoretic epoxy-amine base paint (PPG; WT724 + P962). First, the paint was electro-deposited cathodically at dc voltage of 180 V during 4 min, and then cured at 180 °C for 30 min. After that, the electrode was embedded into an epoxy resin (Buhler; Epoxycure), and worked out to a cylinder shape, the outer diameter of which was 21 mm. The cataphoretic coating allowed avoiding any infiltration of electrolyte between the metal and epoxy resin interface. Only the cross-section of the steel rod embedded in the epoxy resin was used to form a rotating-disk electrode. Just before each experiment, the electrode surface was abraded by emery-paper up to 1200 grade.

CORROSION TEST SOLUTIONS

The corrosion test solution was prepared with de-ionized water and reagent grade chemicals: saturated Ca(OH) 2 + 0.5 M NaCl. This solution simulates the interstitial electrolyte of concrete contaminated by seawater [START_REF] Haleem | Environmental factors affecting the corrosion behavior of reinforcing steel II. Role of some anions in the initiation and inhibition of pitting corrosion of steel in Ca(OH) 2 solutions[END_REF][START_REF] Haran | Studies on Galvanized Carbon Steel in Ca(OH) 2 Solutions[END_REF][START_REF] Andrade | Corrosion of steel reinforcement[END_REF][START_REF] Andrade | Pore solution chemistry and corrosion in hydrated cement systems containing chloride salts: a study of cation specific effects[END_REF][START_REF] Mammoliti | The influence of surface finish of reinforcing steel and ph of the test solution on the chloride threshold concentration for corrosion initiation in synthetic pore solutions[END_REF][START_REF] Alonso | Chloride threshold dependence of pitting potential of reinforcements[END_REF]. The solution pH of the corrosion test solution was 13.

The pre-treatment solution was a 0.1 M phenyl phosphonic acid (PPA). The pH of the phosphonic solution was 1.85. The working electrode was pre-treated during 24 or 72 hours in 0.1 M PPA, and then transferred quickly to the corrosion test solution. The solution was not stirred and all electrochemical tests were performed at room temperature without purging the dissolved oxygen, but the electrochemical cell was closed tightly to minimize carbonation of the solution.

ELECTROCHEMICAL TECHNIQUES

Polarisation measurements

The potentiodynamic polarisation curves were recorded using the Gamry instrument (PC4/300C). Each experiment was carried out with a freshly abraded electrode. Prior to the measurements, the electrode was left at the open-circuit conditions during 2 hours for sufficient potential stabilization. The potential sweep-rate was 10 mV s -1 , and the disk rotation speed was 0, 500, or 1000 rpm. The potential sweep began from the potential slightly more anodic (ca. 20 mV) from the open circuit one (E OC ) for the cathodic polarization curves. Conversely, anodic polarization curves were stated from ca. 20 mV more negative potential than E OC .

Impedance measurements

Impedance (EIS) measurements were performed by using the Gamry equipment (PC4/300C).

The measuring frequency ranged from 100 kHz to 10 mHz with 5 points per decade using a 10 mV (rms) sinusoidal potential perturbation. Experiments were performed in the potentiostatic mode at E OC .

Before any impedance measurements, unless otherwise indicated, the electrode was held at the open circuit conditions during 2 hours for the stabilization of the system with stationary electrode, that is, the disk rotation speed at Ω = 0.

Electrochemical quartz crystal microbalance (EQCM)

For electrochemical quartz crystal microbalance (EQCM) experiments, the iron was plated cathodically (electrodeposited iron) on a quartz crystal blade (6 MHz AT-cut) upon which a thin gold layer was vacuum deposited previously. The surface area of EQCM electrode was 0.22 cm 2 . The quartz crystal electrode was fixed with electrical conducting glue on an epoxy resin reinforced by glass-fiber with electrical circuitry. The iron deposited surface was set outside, and a silicon sealant (Sodiema, Aracolle) allowed avoiding the solution leakage to the quartz backside [START_REF] Bernard | Reduction of silver tarnishing and protection against subsequent corrosion[END_REF].

For the gravimetric measurements, the results are presented as the mass variation with respect to time. The mass changes were derived from the observed frequency changes using the Sauerbrey equation [START_REF] Sauerbrey | Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung[END_REF]:

m K f     s (Eq. 1) f  is the frequency change, m
 is mass change and K s is the constant depending upon the oscillation frequency of the quartz. K s was experimentally determined previously by the electrodeposition of copper [START_REF] Gabrielli | Calibration of the Electrochemical Quartz Crystal Microbalance[END_REF], and was equal to 52 Hz µg -1 cm 2 .

After each test, the surface morphology of the electrode was examined by a scanning electron microscope (SEM; Leica Stereoscan 440), and elemental analyses were performed with EDS (Princeton Gamma-Tech).

RESULTS AND DISCUSSION

POLARIZATION CURVES

Fig. 1 presents the anodic curves obtained for various rotation speeds for carbon steel without and with pre-treatment during 24 or 72 h in 0.1M PPA solution.

<Fig. 1>

For all cases, the polarisation curves exhibit a current plateau, clearly marked for the stationary electrode whereas just an inflexion point is revealed for the highest rotation speed.

Beyond this plateau, a steep current increase was observed. The beginning of this current increase may correspond, in analogy to the passive metal, the critical pitting potential (E pit ).

This potential moves towards more cathodic potentials when the rotation speed decreases.

As for the current density, except for the blank specimen at stationary electrode, it is essentially independent of the rotation speed, values ca. 2 to 3 µA cm -2 . The longer the pretreatment period is, the lower the current plateau is.

In the case of pre-treated steel (Fig. 1b and1c), at the low anodic potential domain, the current density is lower compared with untreated specimens, suggesting that the treatment with PPA acts on the anodic active sites.

<Fig. 2>

It appears that E corr shifts towards more anodic direction when the rotation speed increases for untreated electrode. In contrast, for the electrode treated during 72 hours, E corr is the most negative for the rotation speed of 1000 rpm. Except this case, the longer is the pre-treatment period, the more positive E corr is whatever the electrode rotation speed.

As for E pit , the longer is the pre-treatment duration, the more positive this potential is, indicating the improvement of the carbon steel against the pitting corrosion. Besides, whatever the pre-treatment duration (24 or 72 hours), the increase of the electrode rotation speed moves the pitting potential (E pit ) of steel towards more cathodic values. The increase of the rotation speed may destabilize the passive film by mechanical constraint, shearing effect, exerted on the electrode surface. The increase of the electrode rotation speed may increase the corrosion rate by two effects:

 Due to a faster diffusion of the dissolved oxygen (electrochemical corrosion).

 Due to the shearing by the fluid displacement (erosion).

For pre-treated carbon steel (Fig. 3b and3c), the current plateau observed no longer depends upon the rotation speed. Besides, the current plateau appears at less cathodic potentials than the case of the untreated electrode. This plateau can be related to the diffusion process through the surface film formed by iron oxide and PPA or the adsorption of PPA and does not seem to be limited by the mass transport.

By the comparison of Fig. 3a and 3b or 3c we observe a shift of E corr towards more anodic potentials. That is, the steady-state cathodic current density for the potential domain preceding the current plateau decreases for the pre-treated specimens.

<Fig. 3>

We have applied the Stern -Geary relationship, to evaluate the current density j at the opencircuit potential [START_REF] Stern | Electrochemical Polarization I: A Theoretical Analysis of the Shape of Polarization Curves[END_REF].

j = j corr •{exp[b a •(E-E corr )] -exp[b c (E-E corr )]} (Eq. 2)
where b a and b c are the anodic and cathodic Tafel constant (V -1 ), respectively.

The corrosion current density j corr is then evaluated by a nonlinear regression calculation. The calculation was carried out between about 20mV to anodic and 50mV to cathodic from the corrosion potential [START_REF] Muresan | Protection of bronze covered with patina by innoxious organic substances[END_REF]. Too wide potential range may induce a systematic error, because the system will no longer follow the Tafel law. The results of calculation are presented in Fig. 3d as an example for blank electrode at stationary conditions. It can be seen that the calculated We calculated also the Stern-Geary constant (B) by means of the following relationship:

When E = E corr :   c a corr p 1 d d b b j R E j     , then p corr R B j  , with c a b b B   1 (Eq. 4)
Table 1 summarizes the results of fitting calculations for other experimental conditions. In all cases, the cathodic Tafel constant b c remains essentially constant whereas the anodic Tafel constant b a becomes smaller for pre-treated carbon steel (24 or 72 hours). This phenomenon can be explained by desorption of the inhibitor [START_REF] Dermaj | Electrochemical and spectroscopic evidences of corrosion inhibition of bronze by a triazole derivative[END_REF].

The mean value of constant B , for the untreated carbon steel (blank), was 29.6 ± 0.6 mV.

According to Andrade and al. [START_REF] Andrade | Corrosion Effect of Stray Currents and the Techniques for Evaluating Corrosion of Rebars in Concrete[END_REF], this value is characteristic of corrosion at active state.

B value of pre-treated carbon steel was 48.7 ± 0.7 mV; for both duration of pre-treatment ( 24or 72 hours) corresponding to the corrosion at passive steel rebar, as attested by a small b a value [START_REF] Andrade | Corrosion Effect of Stray Currents and the Techniques for Evaluating Corrosion of Rebars in Concrete[END_REF]. This result is in agreement with that reported by Andrade et al. [START_REF] Andrade | Corrosion rates of steel in concrete[END_REF] and indicates the reliability of our results. <Table 1>

ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY

For the electrochemical impedance spectroscopy (EIS), only the results concerning stationary electrode without or with pre-treated during 72 hours in 0.1M PPA solution will be shown.

Protective effect of pre-treatment

The Fig. 4 shows the impedance spectra in Bode plots of carbon steel without treatment and treated during 72h in 0.1M PPA, obtained after 2 hours of potential stabilization period in the corrosion test solution,. A clear difference is observed between these two diagrams.

For untreated carbon steel, log(|Z|) -log(f) curve shows the slope close to -0.5 for the frequency below 10 Hz, indicating the contribution of the diffusion impedance Z dif . For higher frequencies, in this figure, it seems a contribution of one parallel connection of resistance R and capacitance C. however, a parameter regression calculation showed two ladder R//C are necessary to avoid a systematic error. As a whole, the equivalent circuit presented in Fig. 5a was used to reproduce the experimental results.

In this figure, R e represents the solution resistance. R t and C dl stand for the charge transfer resistance and the double capacitance. R F and C F are associated to the redox process between Fe(II) ↔ Fe(III) in the passive layer.  dl and α F account for the Cole-Cole dispersion of time constants [START_REF] Diaz | Passivity of iron in red mud's water solutions[END_REF] (Fig. 5a). The use of this type of electric circuit is common in corrosion studies of iron in alkaline media [START_REF] Diaz | Passivity of iron in red mud's water solutions[END_REF].

The impedance spectrum obtained with the pre-treated electrode is formed by one capacitive loop. The corrosion rate is decreased markedly, and as attested by Fig. 3b, the diffusion process no longer intervenes in the reaction kinetics. This is the reason that Z dif does not appear in the impedance spectrum. Besides, the pre-treatment makes the surface film much stable, and the redox process mentioned above is slowed down substantially, making the disappearance of R F and C F couple. Consequently, the diagram is adjusted with a simple equivalent electric circuit illustrated in Fig. 5b.

The results of regression calculations with a simplex method for these two systems were overlaid on Fig. 4. The comparison of experimental and calculated spectra corroborates the adequacy of the equivalent circuit adopted.

<Fig. 4> <Fig. 5>

Table 2 shows the values obtained for the circuit elements.

With the pre-treated carbon steel, the value of C dl is markedly smaller corresponding to the presence of adsorbed inhibitor or the complex film formation. The charge transfer resistance R t value increases dramatically for the pre-treated carbon steel, which indicates an efficient protective effect of pre-treatment on carbon steel corrosion in the solution simulating chloride containing concrete medium. With Stern-Geary equation with R t instead of R p , the corrosion current density corr j (Eq. 5), then the protective efficiency PE% (Eq. 3) were calculated [START_REF] Epelboin | A-C impedance measurements applied to corrosion studies and corrosion rate determination[END_REF].

t corr R B j  (Eq. 5)
The B value was that determined by the polarization curve and given in Table 1. The protective effectiveness is higher than 99%. Taking into account of a marked extrapolation to determine R t for pre-treated specimen, this result is in good agreement with that obtained from polarisation studies. <Table 2>

Effect of the immersion time

Fig. 6 shows the time evolution of impedance spectra in the corrosion test solution for the stationary electrode without and with pre-treatment during 72h in 0.1M PPA. In the case of untreated carbon steel (Fig. 6a), the value of whole impedance modulus decreases considerably with time. Besides, as can be seen from the slope of log(|Z|) -log(f) curves close to -0.5 indicating the diffusion impedance below 1 Hz. This behaviour is characteristic of highly active corrosion with appearance and propagation of localized corrosion.

For the pre-treated carbon steel (Fig. 6b), it appears that the phase shift remains high and all diagrams have similar shapes in Nyquit plot, not illustrated here. The impedance behaviour corresponds thus well to that of the passive electrode. They are formed all of them by only one capacitive branch as presented above (Fig. 4). The imaginary component increases with immersion time, that is, the interface capacitance decreases with time. We can conclude that in spite of chloride ions, the passive film increases its thickness because of highly alkaline medium and the film formed on steel surface during the pre-treatment reveals highly satisfactory inhibiting properties. Fig. 1c shows also clearly that E corr is much cathodic than E pit . The efficiency of this protective layer is therefore not altered with time.

<Fig. 6>

GRAVIMETRIC MEASUREMENTS

In this part, the mass and the potential change with respect to time of carbon steel without treatment or the pre-treated specimen (during 72h in a 0.1M PPA) in the corrosion test solution (Figs. 7 and8) will be presented.

In absence of treatment (Fig. 7), the mass decreases slightly (3 µg cm -2 ) for about 6 hours, then decreases continuously with time. As for the potential, it increases during one day, thus can be explained by the effect of highly alkaline medium to the passivation of metal. After two days of immersion, the potential decreases steeply and stabilizes at about -0.55 Vsce after three days immersion. This potential change is a signature of the beginning of a local attack of metal because the protective layer was damaged by chloride ions. This analysis is confirmed by a steep decrease of the electrode mass by EQCM data.

On Fig. 8, the mass and potential changes during the pre-treatment period, 72 hours, are also presented. At 72 hours of immersion, the electrode was transferred into the corrosion test solution. During the pre-treatment (Fig. 8), it appears that the open-circuit potential increases continuously during the whole pre-treatment period. Beyond this period, the potential stabilizes in aggressive solution at about the 10 th day. The mass of the products formed at the interface increases (at the moment of immersing sample after pre-treatment in testing solution): up to 70-100 µg/cm 2 . We think that this is related with the deposition of Ca-PPA complex. This mass increases with time revealing an improvement of the film formed on the steel surface. The observed fluctuations are owed to the temperature fluctuation corresponding to periods of day and night. Indeed, we did not use a thermostatically controlled cell.

<Fig. 7> <Fig.8>

Figs. 9 and 10 present the surface morphology and the results of EDS analysis of the sample without and with pre-treatment, respectively, at the end of the test. In the case of blank sample, a local attack of the steel surface as pitting by chloride ions can be observed clearly.

On the contrary, with pre-treatment, almost no corrosion is revealed; some precipitates (white heap in SEM picture) are appeared probably because of insufficient surface rinsing and this precipitate will be not highly soluble such as CaCO 3 . The EDS analysis showed the presence of the phosphorus, likely due to the formation of Fe -PPA complex.

<Fig. 9>

<Fig. 10>

CONCLUSIONS

To evaluate the protective effectiveness of pre-treatment by immersion of carbon steel reinforcement in 0.1M Phenyl Phosphonic Acid (PPA), various experimental methods were applied. The experiments were carried out in an aqueous electrolyte medium simulating interstitial concrete pore solution contaminated by seawater:

1. Polarisation curves showed that the pre-treatment of carbon steel induces a decrease of the corrosion current density, and the corrosion potential shift towards more anodic direction.

Therefore, the passive state of low carbon steel is improved substantially by the pretreatment.

2. Impedance measurements provided information related to the formation and evolution of the protective layer. The pre-treatment of carbon steel during 72h in 0.1 M PPA solution allows the formation of an efficient protective layer on the surface against the corrosion of carbon steel in alkaline chloride media. The protective effectiveness is estimated higher than 99%.

3. Gravimetric measurements obtained by electrochemical quartz crystal microbalance (EQCM) show that the mass of the products formed at the interface increases with time revealing a thickening of the film formed on the steel surface.

4. EDS analysis indicated the presence of the phosphorus on the steel surface, suggesting the presence of Fe -PPA complex.

Fig. 2

 2 Fig.2presents the variation of the corrosion potential (E corr ), the pitting potential (E pit ) and the width of the passive range (E = E pit -E corr ) with respect to the pre-treatment duration in 0.1 M PPA for different rotation speeds.

Fig. 2c presents

  Fig.2cpresents the width of the passive range (E) calculated from the difference between E pit and E corr (E = E pit -E corr ). We remarked that E decreases when the electrode rotation speed increases. The largest passive range (~1V) was observed for the electrode pre-treated during 72 hours for stationary electrode. The narrowest passive domain was observed, on the contrary, for the fastest rotating electrode with untreated carbon steel electrode.The cathodic polarisation curves obtained for various rotation speeds for carbon steel without and with treatment during 24 or 72 hours in 0.1 M PPA solution are presented in Fig.3. For blank specimens (untreated carbon steel), the curve exhibits a large diffusion plateau between ca -0.8 and -1.2 V. The height of this plateau is dependent on the electrode rotation speed. The diffusion plateau was assigned to the reduction of dissolved oxygen involving the transfer of 4e -occurring on an almost uniformly active surface.

(

  curve) and experimental data (open circles) overlap very well. The correlation coefficient R 2 was equal to 0.99993.The protective efficiency (PE) in percent was evaluated from the following equation: current densities for untreated and pre-treated specimens, respectively. The highest protective efficiency was obtained with the pre-treated electrode during 72 hours in 0.1 M PPA solution for stationary electrode (0 rpm); it reaches 97%.

Figure captions:Fig. 1 :Fig. 2 :Fig. 3 :

 123 Figure captions:

Fig. 4 :

 4 Fig. 4: EIS spectra of untreated and 72 hours pre-treated carbon steel then immersed during 2 hours in the corrosion test solution.

Fig. 5 :Fig. 6 :Fig. 7 :Fig. 8 :Fig. 9 :

 56789 Fig. 5: Equivalent electrical circuit reproducing the results presented on Fig. 4 (a) for the untreated carbon steel (b) for pre-treated carbon steel.

Fig. 10 :Fig. 4 :Fig. 5 :

 1045 Fig. 10: SEM picture and EDS analysis of the 72h pre-treated sample in the end of electrochemical quartz crystal microbalance test.

Table 1 :

 1 Results of regression calculation for the evaluation of corrosion parameters fromcathodic potential scan data.

		Ω/rpm	E corr /V/ECS	j corr /nA/cm 2	PE(%)	ba/V -1	bc/V -1	B/mV
		0	-0.36	110	-	18.4	-16	28.5
	Blank	500	-0.37	129	-	18.9	-13	30.5
		1000	-0.39	216	-	19.7	-13	29.9
		0	-0.27	6.76	93.9	4.73	-15	48.4
	24h pre-treated	500	-0.26	15.1	88.3	5.07	-15	49.2
		1000	-0.32	19.4	91.0	5.43	-15	47.9
		0	-0.23	3.16	97.1	4.83	-16	47.4
	72h pre-treated	500	-0.23	11.6	91.0	5.17	-14	49.9
		1000	-0.23	15.6	92.8	5.35	-15	48.7

Table 2 :

 2 Parameters obtained by fitting experimental impedance data presented in Fig.4

		R t (kΩ.cm 2 )	C dl (µF.cm -2 )	R F (kΩ.cm 2 )	C F (mF.cm -2 )	R d (kΩ.cm 2 )	 d (s)	j corr (µA cm -2	)	PE (%)
	Untreated	7.53	210	11.5	35	16.5	14.80	3.78	-
	pre-treated	1700	25.1	-	-	-	-	0.03	99.2

ACKNOWLEDGEMENTS

The authors acknowledge the financial support received from the French embassy in Tunisia that allowed among others the trainees of NE at LISE, and to develop the experiments.


Dominique Fourer
email: fourer@labri.fr

Jean-Luc Rouas
email: rouas@labri.fr

Pierre Hanna
email: hanna@labri.fr

Matthias Robine
email: robine@labri.fr

AUTOMATIC TIMBRE CLASSIFICATION OF ETHNOMUSICOLOGICAL AUDIO RECORDINGS

Automatic timbre characterization of audio signals can help to measure similarities between sounds and is of interest for automatic or semi-automatic databases indexing. The most effective methods use machine learning approaches which require qualitative and diversified training databases to obtain accurate results. In this paper, we introduce a diversified database composed of worldwide nonwestern instruments audio recordings on which is evaluated an effective timbre classification method. A comparative evaluation based on the well studied Iowa musical instruments database shows results comparable with those of state-of-the-art methods. Thus, the proposed method offers a practical solution for automatic ethnomusicological indexing of a database composed of diversified sounds with various quality. The relevance of audio features for the timbre characterization is also discussed in the context of non-western instruments analysis.

INTRODUCTION

Characterizing musical timbre perception remains a challenging task related to the human auditory mechanism and to the physics of musical instruments [START_REF] Fletcher | The Physics of Musical Instruments[END_REF]. This task is full of interest for many applications like automatic database indexing, measuring similarities between sounds or for automatic sound recognition. Existing psychoacoustical studies model the timbre as a multidimensional phenomenon independent from musical parameters (e.g. pitch, duration or loudness) [START_REF] Grey | Perceptual effects of spectral modifications on musical timbre[END_REF][START_REF] Mcadams | Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes[END_REF]. A quantitative interpretation of instrument's timbre based on acoustic features computed from audio signals was first proposed in [START_REF] Misdariis | Validation of a multidimensional distance model for perceptual dissimilarities among musical timbres[END_REF] and pursued in more recent studies [START_REF] Peeters | The timbre toolbox: Audio descriptors of musical signals[END_REF] which aim at organizing audio timbre descriptors efficiently. Nowadays, effective automatic timbre classification methods [START_REF] Peeters | Automatically selecting signal descriptors for sound classification[END_REF] use supervised statistical learning approaches based on audio signals features computed from analyzed data. Thus, the performance obtained with such systems depends on the taxonomy, the size and the diversity of training databases. However, most of existing research databases (e.g. RWC [START_REF] Goto | Rwc music database: Music genre database and musical instrument sound database[END_REF], Iowa [START_REF] Fritts | Musical instrument samples[END_REF]) are only composed of common western instruments annotated with specific taxonomies. In this work, we revisit the automatic instrument classification problem from an ethnomusicological point of view by introducing a diversified and manually annotated research database provided by the Centre de Recherche en Ethno-Musicologie (CREM). This database is daily supplied by researchers and has the particularity of being composed of uncommon non-western musical instrument recordings from around the world. This work is motivated by practical applications to automatic indexing of online audio recordings database which have to be computationally efficient while providing accurate results. Thus, we aim at validating the efficiency and the robustness of the statistical learning approach using a constrained standard taxonomy, applied to recordings of various quality. In this study, we expect to show the database influence, the relevance of timbre audio features and the choice of taxonomy for the automatic instrument classification process. A result comparison and a crossdatabase evaluation is performed using the well-studied university of Iowa musical instrument database. This paper is organized as follows. The CREM database is introduced in Section 2. The timbre quantization principle based on mathematical functions describing audio features is presented in Section 3. An efficient timbre classification method is described in Section 4. Experiments and results based on the proposed method are detailed in Section 5. Conclusion and future works are finally discussed in Section 6.

THE CREM ETHNOMUSICOLOGICAL DATABASE

The CREM research database1 is composed of diversified sound samples directly recorded by ethnomusicologists in various conditions (i.e. no recording studio) and from diversified places all around the world. It contains more than 7000 hours of audio data recorded since 1932 to nowadays using different supports like magnetic tapes or vinyl discs.

The vintage audio recordings of the database were carefully digitized to preserve the authenticity of the originals and contain various environment noise. The more recent audio recordings can be directly digital recorded with a high-quality. Most of the musical instruments which com-pose this database are non-western and can be uncommon while covering a large range of musical instrument families (see Figure 1(a)). Among uncommon instruments, one can find the lute or the Ngbaka harp as cordophones. More uncommon instruments like Oscillating bamboo, struck machete and struck girder were classified by ethnomusicologists as idiophones. In this paper, we restricted our study to the solo excerpts (where only one monophonic or polyphonic instrument is active) to reduce the interference problems which may occur during audio analysis. A description of the selected CREM sub-database is presented in

TIMBRE QUANTIZATION AND CLASSIFICATION

Timbre quantization

Since preliminaries works on the timbre description of perceived sounds, Peeters et al. proposed in [START_REF] Peeters | The timbre toolbox: Audio descriptors of musical signals[END_REF] a large set of audio features descriptors which can be computed from audio signals. The audio descriptors define numerical functions which aim at providing cues about specific acoustic features (e.g. brightness is often associated with the spectral centroid according to [START_REF] Schubert | Spectral centroid and timbre in complex, multiple instrumental textures[END_REF]). Thus, the audio descriptors can be organized as follows:

• Temporal descriptors convey information about the time evolution of a signal (e.g. log attack time, temporal increase, zero-crossing rate, etc.).

• Harmonic descriptors are computed from the detected pitch events associated with a fundamental frequency (F 0 ). Thus, one can use a prior waveform model of quasi-harmonic sounds which have an equally spaced Dirac comb shape in the magnitude spectrum. The tonal part of sounds can be isolated from signal mixture and be described (e.g. noisiness, inharmonicity, etc.).

• Spectral descriptors are computed from signal timefrequency representation (e.g. Short-Term Fourier Transform) without prior waveform model (e.g. spectral centroid, spectral decrease, etc.)

• Perceptual descriptors are computed from auditoryfiltered bandwidth versions of signals which aim at approximating the human perception of sounds. This can be efficiently computed using Equivalent Rectangular Bandwidth (ERB) scale [START_REF] Moore | Suggested formulae for calculating auditory-filter bandwidths and excitation patterns[END_REF] which can be combined with gammatone filter-bank [START_REF] Ambikairajah | Wideband speech and audio coding using gammatone filter banks[END_REF] (e.g. loudness, ERB spectral centroid, etc.)

In this study, we focus on the sound descriptors listed in table 2 which can be estimated using the timbre toolbox2 and detailed in [START_REF] Peeters | The timbre toolbox: Audio descriptors of musical signals[END_REF]. All descriptors are computed for each analyzed sound excerpt and may return null values. The harmonic descriptors of polyphonic sounds are computed using the prominent detected F 0 candidate (single F 0 estimation). To normalize the duration of analyzed sound, we separated each excerpt in 10-seconds length segments without distinction of silence or pitch events. Thus, each segment is represented by a real vector where the corresponding time series of each descriptor is summarized by a statistic. The median and the Inter Quartile Range (IQR) statistics were chosen for their robustness to outliers.

Classification taxonomy

In this study, we use two databases which can be annotated using different taxonomies. Due to its diversity, the CREM database was only annotated using the Hornbostel and Sachs taxonomy [START_REF] Hornbostel | The classification of musical instruments[END_REF] (T1) illustrated in Figure 1(a) which is widely used in ethnomusicology. This hierarchical taxonomy is general enough to classify uncommon instruments (e.g. struck bamboo) and conveys information about sound production materials and playing styles. From an another hand, the Iowa musical instruments database [START_REF] Fritts | Musical instrument samples[END_REF] used in our experiments was initially annotated using a musician's instrument taxonomy (T2) as proposed in [START_REF] Peeters | Automatically selecting signal descriptors for sound classification[END_REF] and illustrated in Figure 1(b). This database is composed of common western pitched instruments which can easily be annotated using T1 as described in Table 3. Content of the Iowa database using musician's instrument taxonomy (T2) and equivalence with the Hornbostel and Sachs taxonomy (T1).

AUTOMATIC INSTRUMENT TIMBRE CLASSIFICATION METHOD

The described method aims at estimating the corresponding taxonomy class name of a given input sound.

Method overview

Here, each sound segment (cf. Section 3.1) is represented by vector of length p = 164 where each value corresponds to a descriptor (see Table 2). The training step of this method (illustrated in Figure 2) aims at modeling each timbre class using the best projection space for classification.

A features selection algorithm is first applied to efficiently reduce the number of descriptors to avoid statistical overlearning. The classification space is computed using discriminant analysis which consists in estimating optimal weights over the descriptors allowing the best discrimination between timbre classes. Thus, the classification task consists in projecting an input sound into the best classification space and to select the most probable timbre class using the learned model.

Linear discriminant analysis

The goal of Linear Discriminant Analysis (LDA) [START_REF] Anderson | An Introduction to Multivariate Statistical Analysis[END_REF] is to find the best projection or linear combination of all descriptors which maximizes the average distance between classes (inter-class distance) while minimizing distance between individuals from the same class (intra-class distance). This method assumes that the class affectation of each individual is a priori known. Its principle can be described as follows. First consider the n × p real matrix M where each row is a vector of descriptors associated to a sound (individual). We assume that each individual is a member of a unique class k ∈ [1, K]. Now we define W as the intraclass variance-covariance matrix which can be estimated by:

W = 1 n K k=1 n k W k , (1)
where W k is the variance-covariance matrix computed from the n k × p sub-matrix of M composed of the n k individuals included into the class k.

We also define B the inter-class variance-covariance matrix expressed as follows:

B = 1 n K k=1 n k (µ k -µ)(µ k -µ) T , (2)
where µ k corresponds to the mean vector of class k and µ is the mean vector of the entire dataset. According to [START_REF] Anderson | An Introduction to Multivariate Statistical Analysis[END_REF], it can be shown that the eigenvectors of matrix D = (B + W ) -1 B solve this optimization problem. When the matrix A = (B + W ) is not invertible, a computational solution consists in using pseudoinverse of matrix A which can be calculated using A T (AA T ) -1 .

Features selection algorithms

Features selection aims at computing the optimal relevance of each descriptor which can be measured with a weight or a rank. The resulting descriptors subset has to be the most discriminant as possible with the minimal redundancy. In this study, we investigate the three approaches described below.

LDA features selection

The LDA method detailed in Section 4.2 can also be used for selecting the most relevant features. In fact, the computed eigenvectors which correspond to linear combination of descriptors convey a relative weight applied to each descriptor. Thus, the significance (or weight) S d of a descriptor d can be computed using a summation over a defined range [1, R] of the eigenvectors of matrix D as follows:

S d = R r=1 |v r,d |, (3)
where v r,d is the d-th coefficient of the r-th eigenvector associated to the eigenvalues sorted by descending order (i.e. r = 1 corresponds to the maximal eigenvalue of matrix D). In our implementation, we fixed R = 8.

Mutual information

Features selection algorithms aim at computing a subset of descriptors that conveys the maximal amount of information to model classes. From a statistical point of view, if we consider classes and feature descriptors as realizations of random variables C and F . The relevance can be measured with the mutual information defined by:

I(C, F ) = c f P (c, f ) P (c, f ) P (c)P (f ) , (4)
where P (c) denotes the probability of C = c which can be estimated from the approximated probability density functions (pdf) using a computed histogram. According to Bayes theorem one can compute P (c, f ) = P (f |c)P (c) where P (f |c) is the pdf of the feature descriptor value f into class c. This method can be improved using [START_REF] Battiti | Using mutual information for selecting features in supervised neural net learning[END_REF] by reducing simultaneously the redundancy by considering the mutual information between previously selected descriptors.

Inertia Ratio Maximisation using features space projection (IRMFSP)

This algorithm was first proposed in [START_REF] Peeters | Automatic classification of large musical instrument databases using hierarchical classifiers with intertia ratio maximization[END_REF] to reduce the number of descriptors used by timbre classification methods. It consists in maximizing the relevance of the de-scriptors subset for the classification task while minimizing the redundancy between the selected ones. This iterative method (ι ≤ p) is composed of two steps. The first one selects at iteration ι the non-previously selected descriptor which maximizes the ratio between inter-class inertia and the total inertia expressed as follow:

d(ι) = arg max d K k=1 n k (µ d,k -µ d )(µ d,k -µ d ) T n i=1 (f (ι) d,i -µ d )(f (ι) d,i -µ d ) T , (5) where f (ι
f (ι+1) d = f (ι) d -f (ι) d • g d g d ∀d = d(ι) , (6) where f (ι)
d is the vector of the previously selected descriptor d(ι) for all the individuals of the entire dataset and

g d = f (ι) d / f (ι) d
is its normalized form.

Class modeling and automatic classification

Each instrument class is modeled into the projected classification space resulting from the application of LDA. Thus, each class can be represented by its gravity center μk which corresponds to the vector of the averaged values of the projected individuals which compose the class k. The classification decision which affect a class k to an input sound represented by a projected vector x is simply performed by minimizing the Euclidean distance with the gravity center of each class as follows:

k = arg min k μk -x 2 ∀k ∈ [1, K], (7)
where v 2 denotes the l 2 norm of vector v. Despite its simplicity, this method seems to obtain good results comparable with those of the literature [START_REF] Peeters | The timbre toolbox: Audio descriptors of musical signals[END_REF].

EXPERIMENTS AND RESULTS

In this section we present the classification results obtained using the proposed method described in Section 4.

Method evaluation based on self database classification

In this experiment, we evaluate the classification of each distinct database using different taxonomies. We applied the 3-fold cross validation methodology which consists in partitioning the database in 3 distinct random subsets composed with 33% of each class (no collision between sets). Thus, the automatic classification applied on each subset is based on training applied on the remaining 66% of the database. Figure 5.1 compares the classification accuracy obtained as a function of the number of used descriptors. The resulting confusion matrix of the CREM database using 20 audio descriptors is presented in Table 4 and shows an average classification accuracy of 80% where each instrument is well classified with a minimal accuracy of 70% for the aerophones. These results are good and seems comparable with those described in the literature [START_REF] Peeters | Automatic classification of large musical instrument databases using hierarchical classifiers with intertia ratio maximization[END_REF] using the same number of descriptor. The most relevant feature descriptors (selected among the top ten) estimated by the IRMSFP and used for the classification task are detailed in Table 7. This result reveals significant differences between the two databases. As an example, harmonic descriptors are only discriminative for the CREM database but not for the Iowa database. This may be explained by the presence of membranophone in the CREM database which are not present in the Iowa database. Contrarily, spectral and perceptual descriptors seems more relevant for the Iowa database than for the CREM database. Some descriptors appear to be relevant for both database like the Spectral flatness (Sflat) and the ERB scale frame energy (ErbFErg) which describe the spectral envelope of signal. Table 4. Confusion matrix (expressed in percent of the sounds of the original class listed on the left) of the CREM database using the 20 most relevant descriptors selected by IRMSFP.

Cross-database evaluation

In this experiments (see Table 5), we merged the two databases and we applied the 3-fold cross validation method based on the T1 taxonomy to evaluate the classification accuracy on both database. The resulting average accuracy is about 68% which is lower than the accuracy obtained on the distinct classification of each database. The results of cross-database evaluation applied between databases using the T1 taxonomy are presented in Table 6 and obtain a poor average accuracy of 30%. This seems to confirm our intuition that the Iowa database conveys insufficient information to distinguish the different playing styles between the non-western cordophones instruments of the CREM database.

CONCLUSION AND FUTURE WORKS

We applied a computationally efficient automatic timbre classification method which was successfully evaluated on an introduced diversified database using an ethnomusicological taxonomy. This method obtains good classification results (> 80% of accuracy) for both evaluated databases which are comparable to those of the literature. However, the cross-database evaluation shows that each database cannot be used to infer a classification to the other. This can be explained by significant differences between these databases. Interestingly, results on the merged database obtain an acceptable accuracy of about 70%. As shown in previous work [START_REF] Peeters | Automatic classification of large musical instrument databases using hierarchical classifiers with intertia ratio maximization[END_REF], our experiments confirm the efficiency of IRMFSP algorithm for automatic features selection applied to timbre classification. The interpretation of the most relevant selected features shows a significant effect of the content of database rather than on the taxonomy. However the timbre modeling interpretation applied to timbre classification remains difficult. Future works will consist in further investigating the role of descriptors by manually constraining selection before the classification process.

Figure 1 .

 1 Figure 1. Taxonomies used for the automatic classification of musical instruments as proposed by Hornbostel and Sachs taxonomy in [16] (a) and Peeters in [13] (b).

Figure 2 .

 2 Figure 2. Training step of the proposed method.

  ) d,i denotes the value of descriptor d ∈ [1, p] affected to the individual i. µ d,k and µ d respectively denote the average value of descriptor d into the class k and for the total dataset. The second step of this algorithm aims at orthogonalizing the remaining data for the next iteration as follows:

Figure 3 .

 3 Figure 3. Comparison of the 3-fold cross validation classification accuracy as a function of the number of optimally selected descriptors.

Table 1

 1

	. According to this table, one can observe that
	this database is actually inhomogeneous. The aerophones
	are overrepresented while membranophones are underrep-
	resented. Due to its diversity and the various quality of
	the composing sounds, the automatic ethnomusicological
	classification of this database may appear as challenging.
	Class name	Duration (s)	#
	aerophones-blowed		1,383	146
	cordophones-struck	357		37
	cordophones-plucked	715	1,229	75	128
	cordophones-bowed	157		16
	idiophones-struck	522		58
	idiophones-plucked	137	753	14	82
	idiophones-clinked	94		10
	membranophones-struck		170	19
	Total		3,535	375

Table 1 .

 1 Content of the CREM sub-database with duration and number of 10-seconds segmented excerpts.

Table 2 .

 2 Acronym, name and number of the used timbre descriptors.

	Acronym	Descriptor name	#
	Att	Attack duration (see ADSR model [15])	1
	AttSlp	Attack slope (ADSR)	1
	Dec	Decay duration (ADSR)	1
	DecSlp	Decay slope (ADSR)	1
	Rel	Release duration (ADSR)	1
	LAT	Log Attack Time	1
	Tcent	Temporal centroid	1
	Edur	Effective duration	1
	FreqMod, AmpMod	Total energy modulation (frequency,amplitude)	2
	RMSenv	RMS envelope	2
	ACor	Signal Auto-Correlation function (12 first coef.)	24
	ZCR	Zero-Crossing Rate	2
	HCent	Harmonic spectral centroid	2
	HSprd	Harmonic spectral spread	2
	HSkew	Harmonic skewness	2
	HKurt	Harmonic kurtosis	2
	HSlp	Harmonic slope	2
	HDec	Harmonic decrease	2
	HRoff	Harmonic rolloff	2
	HVar	Harmonic variation	2
	HErg, HNErg, HFErg,	Harmonic energy, noise energy and frame energy	6
	HNois	Noisiness	2
	HF0	Fundamental frequency F 0	2
	HinH	Inharmonicity	2
	HTris	Harmonic tristimulus	6
	HodevR	Harmonic odd to even partials ratio	2
	Hdev	Harmonic deviation	2
	SCent, ECent	Spectral centroid of the magnitude and energy spectrum	4
	SSprd, ESprd	Spectral spread of the magnitude and energy spectrum	4
	SSkew, ESkew	Spectral skewness of the magnitude and energy spectrum	4
	SKurt, EKurt	Spectral kurtosis of the magnitude and energy spectrum	4
	SSlp, ESlp	Spectral slope of the magnitude and energy spectrum	4
	SDec, EDec	Spectral decrease of the magnitude and energy spectrum	4
	SRoff, ERoff	Spectral rolloff of the magnitude and energy spectrum	4
	SVar, EVar	Spectral variation of the magnitude and energy spectrum	4
	SFErg, EFErg	Spectral frame energy of the magnitude and energy spectrum	4
	Sflat, ESflat	Spectral flatness of the magnitude and energy spectrum	4
	Scre, EScre	Spectral crest of the magnitude and energy spectrum	4
	ErbCent, ErbGCent	ERB scale magnitude spectrogram / gammatone centroid	4
	ErbSprd, ErbGSprd	ERB scale magnitude spectrogram / gammatone spread	4
	ErbSkew, ErbGSkew	ERB scale magnitude spectrogram / gammatone skewness	4
	ErbKurt, ErbGKurt	ERB scale magnitude spectrogram / gammatone kurtosis	4
	ErbSlp, ErbGSlp	ERB scale magnitude spectrogram / gammatone slope	4
	ErbDec, ErbGDec	ERB scale magnitude spectrogram / gammatone decrease	4
	ErbRoff, ErbGRoff	ERB scale magnitude spectrogram / gammatone rolloff	4
	ErbVar, ErbGVar	ERB scale magnitude spectrogram / gammatone variation	4
	ErbFErg, ErbGFErg	ERB scale magnitude spectrogram / gammatone frame energy	4
	ErbSflat, ErbGSflat	ERB scale magnitude spectrogram / gammatone flatness	4
	ErbScre, ErbGScre	ERB scale magnitude spectrogram / gammatone crest	4
	Total		164

Table 3 .

 3 One can notice that the Iowa database is only composed of aerophones and cordophones instruments. If we consider the playing style, only 4 classes are represented if we apply T1 taxonomy to the Iowa database.

	T1 class name T2 equivalence	Duration (s)	#
	aero-blowed	reed/flute and brass 5,951	668
	cordo-struck	struck strings	5,564	646
	cordo-plucked plucked strings	5,229	583
	cordo-bowed	bowed strings	7,853	838
	Total		24,597	2,735

Table 5 .

 5 Confusion matrix (expressed in percent of the sounds of the original class listed on the left) of the evaluated fusion between the CREM and the Iowa database using the 20 most relevant descriptors selected by IRMSFP.

		aero	c-struc	c-pluc	c-bowed	i-pluc	i-struc	i-clink	membr
	aero	74	14	5	3	2	1
	c-struc	12	69	10	5	1			2
	c-pluc	1	7	58	29	1	2		2
	c-bowed	3	6	33	52	1	3
	i-pluc		7		14	79
	i-struc	2	2	4	11	2	51		30
	i-clink	11						89
	membr				6		17		78
				aero	c-struc	c-pluc	c-bowed
			aero	72	9	10	9
			c-struc	12	12	34	42
			c-pluc	23	47	28	3
			c-bowed	28	34	24	14

Table 6 .

 6 Confusion matrix (expressed in percent of the sounds of the original class listed on the left) of the CREM database classification based on Iowa database training.

	CREM T1	Iowa T1	Iowa T2	CREM+Iowa T1
	Edur	AttSlp	AttSlp	AmpMod
	Acor	Dec	Acor	Acor
			ZCR	RMSenv
	Hdev
	Hnois
	HTris3
	Sflat	SFErg	Sflat	Sflat
		ERoff	SRoff	SVar
			SSkew	SKurt
				Scre
	ErbGKurt	ErbKurt	ErbSprd
		ErbFErg	ErbFErg	ErbFErg
		ErbRoff		ErbRoff
		ErbSlp	ErbGSprd
		ErbGCent

Table 7 .

 7 Comparison of the most relevant descriptors estimated by IRMFSP.

CREM audio archives freely available online at: http://archives.crem-cnrs.fr/

MATLAB code available at http://www.cirmmt.org/research/tools

ACKNOWLEDGMENTS

This research was partly supported by the French ANR (Agence Nationale de la Recherche) DIADEMS (Description,Indexation, Acces aux Documents Ethnomusicologiques et Sonores) project (ANR-12-CORD-0022).