EP2419900B1 - Method and device for the objective evaluation of the voice quality of a speech signal taking into account the classification of the background noise contained in the signal - Google Patents

Description

The present invention relates generally to the processing of speech signals and in particular the voice signals transmitted in telecommunications systems. In particular, the invention relates to a method and a device for objectively evaluating the speech quality of a speech signal taking into account the classification of the background noise contained in the signal. The invention applies in particular to speech signals transmitted during a telephone call through a communication network, for example a mobile telephony network or a switched network or packet network telephony network.

In the field of voice communication, the noise included in a speech signal, referred to as "background noise", may include various noises: sounds from engines (cars, motorcycles), aircraft passing through the sky, conversation / whispering noises - for example in a restaurant or café environment -, music, and many other audible noises. In some cases, background noise may be an additional element of communication that can provide useful information to listeners (mobility context, geographic location, environment sharing).

Since the advent of mobile telephony, the ability to communicate from any location has helped to increase the presence of background noise in transmitted speech signals, and has therefore made it necessary to process background noise so maintain an acceptable level of communication quality. Moreover, in addition to noises coming from the environment where the sound is taken, unwanted noise, produced in particular during the coding and transmission of the audio signal on the network (packet losses for example, voice over IP) can also interact with background noise.

In this context, it can be assumed that the perceived quality of the transmitted speech is dependent on the interaction between the different types of background noise. Thus, the document: " Influence of informational content of VoIP application "(hereinafter" Document [1] "), by A. Leman, J. Faure and E. Parizet - article presented at the conference" Acoustics '08 held in Paris from June 29 to July 4, 2008 - describes subjective tests which not only show that the noise level of background noise plays a major role in the assessment of voice quality in the context of a Voice over IP (VoIP) application, but also demonstrate that the type Background noise (environmental noise, line noise, etc.) that is superimposed on the speech signal (the useful signal) plays an important role in evaluating the voice quality of the communication.

The figure 1 annexed to this description, is derived from the above-mentioned Document [1] (see section 3.5, Figure 2 of this document) and represents the opinion means (MOS LQSN) with the associated confidence interval, calculated from notes given by auditors to audio messages containing six different types of background noise, according to the ACR method. (Absolute Category Rating). The various types of noise are: pink noise, stationary speech noise (BPS), electrical noise, city noise, restaurant noise, television noise or voice, each noise being considered at three different levels of perceived loudness.

The horizontal line above the other curves represents the notation corresponding to an audio signal containing no background noise. The notes given, "MOS LQSN" - for "Mean Opinion Score of Listening Quality and Subjective Method for Narrow Band Signal" - are in accordance with the ITU-T Recommendations P. 800 and P. 800.1, respectively, entitled , "Methods for subjective determination of transmission quality" and "Mean Opinion Score (MOS) terminology". As can be seen on the figure 1 , the notes given for the same useful signal (ie the signal of speech contained in the audio signal tested) vary not only according to the type of background noise contained in the audio signal, but also according to the perceived loudness (loudness) of a background noise considered.

However, to date, the type of background noise present in a given audio signal is not taken into account in the known methods of objective evaluation of the speech quality of a speech signal, whether for example, the PESO model (see ITU-T Rec., P.862), Model E (described, for example, in ITU-T Rec. ITU-T, G.107 "The E-model, a computational model for use in transmission planning", 2003 ), or even non-intrusive methods like the one described in the document " P.563-The ITU-T Standard for Single-Ended Speech Quality Assessment, by L. Malfait, J. Berger, and M. Kastner, IEEE Transaction on Audio, Speech, and Language Processing, Vol 14 (6), pp. 1924-1934, 2006 .

It is also known according to the document EP1288914A2 an objective quality measurement method according to ITU-T standard P.862 "PESQ" including a correction of the quality measurement taking into account the intensity of the background noise.

It is also known according to the document US5,84,921 an audio signal transmission method determining the quality of said signal by classifying the signal according to the noise level present therein as non-noisy, slightly noisy, noisy and highly noisy.

Thus, in view of the foregoing, there is a real need for an objective voice quality evaluation model, taking into account the type of background noise present in an audio signal to be evaluated.

• classification du bruit de fond contenu dans le signal de parole selon un ensemble prédéfini de classes de bruits de fond ;
• évaluation de la qualité vocale du signal de parole, en fonction d'au moins la classification obtenue relative au bruit de fond présent dans le signal de parole.

The present invention aims in particular to meet the aforementioned need, proposing in a first aspect a method of objective evaluation of the voice quality of a speech signal. According to the invention, this method comprises the steps of:

• classification of the background noise contained in the speech signal according to a predefined set of background noise classes;
• voice quality evaluation of the speech signal, based on at least the obtained classification relating to the background noise present in the speech signal.

According to the invention, taking into account the type of background noise present in the speech signal in the objective evaluation of the speech signal's speech quality, makes it possible to obtain a quality assessment closer to the evaluation. voice quality - that is, the quality actually perceived by users - than the known methods of objective evaluation of voice quality.

• estimation de la sonie totale (N) du signal de bruit (SIG_N) ;
• calcul d'une note de qualité vocale en fonction de la classe de bruit de fond présent dans le signal de parole, et de la sonie totale estimée pour le signal de bruit.

According to one embodiment of the invention, the step of evaluating the voice quality of the signal, of speech, comprises the steps of:

• estimation of the total loudness (N) of the noise signal (SIG_N);
• calculating a voice quality score according to the background noise class present in the speech signal, and the estimated total loudness for the noise signal.

In practice, a voice quality score (MOS_CLi) according to the invention is obtained according to a mathematical formula of the following general form: $$\mathit{MOS\_CLi}={\mathrm{VS}}_{i-1}+{\mathrm{VS}}_i\times f\left(\mathrm{NOT}\right)$$

• ■ MOS_CLi est la note calculée pour le signal de bruit ;
• ■ f(N) est une fonction mathématique de la sonie totale, N, estimée pour le signal de bruit ;
• ■ C_i-1 et C_i sont deux coefficients définis pour la classe (CLi) de bruit de fond obtenue pour le signal de bruit.

Or:

• ■ MOS_CLi is the calculated score for the noise signal;
• ■ f (N) is a mathematical function of the total loudness, N , estimated for the noise signal;
• ■ C _i-1 and C _i are two coefficients defined for the class (CLi) of background noise obtained for the noise signal.

More particularly, according to a particular embodiment of the invention, the function f (N) is the natural logarithm, Ln (N), of the total loudness N expressed in sones.

In particular, according to an embodiment of the invention, the total loudness of the noise signal is estimated according to an objective model of loudness estimation, for example the Zwicker model or the Moore model.

• extraction du signal de parole, d'un signal de bruit de fond, dit signal de bruit ;
• calcul de paramètres audio du signal de bruit ;
• classification du bruit de fond contenu dans le signal de bruit, en fonction des paramètres audio calculés, selon ledit ensemble de classes de bruits de fond.

According to other embodiments of the invention, the step of classifying the background noise contained in the speech signal includes the steps of:

• extracting the speech signal, a background noise signal, said noise signal;
• calculation of audio parameters of the noise signal;
• classification of the background noise contained in the noise signal, according to the calculated audio parameters, according to said set of background noise classes.

According to a particular embodiment of the invention, the step of calculating audio parameters of the noise signal comprises the calculation of a first parameter (IND_TMP), called temporal indicator, relating to the temporal evolution of the noise signal, and a second parameter (IND_FRQ), called frequency indicator, relating to the frequency spectrum of the noise signal.

In practice, the time indicator (IND_TMP) is obtained from a calculation of the variation of the sound level of the noise signal, and the frequency indicator (IND_FRQ) is obtained from a calculation of variation of the amplitude of the frequency spectrum of the noise signal.

The combination of these two indicators makes it possible to obtain a low rate of classification errors, whereas their computation consumes little computing resources.

• comparer la valeur de l'indicateur temporel (IND_TMP) obtenue pour le signal de bruit à un premier seuil (TH1), et déterminer en fonction du résultat de cette comparaison que le signal de bruit est stationnaire ou non ;
• lorsque le signal de bruit est identifié comme non-stationnaire, comparer la valeur de l'indicateur fréquentiel à un second seuil (TH2), et déterminer en fonction du résultat de cette comparaison que le signal de bruit appartient à une première classe ou à une seconde classe de bruits de fond ;
• lorsque le signal de bruit est identifié comme stationnaire, comparer la valeur de l'indicateur fréquentiel à un troisième seuil (TH3), et déterminer en fonction du résultat de cette comparaison que le signal de bruit appartient à une troisième classe ou à une quatrième classe de bruits de fond.

According to a particular implementation of the aforementioned classification step, to perform this classification of the background noise associated with the noise signal, the method of the invention implements steps consisting of:

• comparing the value of the time indicator (IND_TMP) obtained for the noise signal with a first threshold (TH1), and determining according to the result of this comparison that the noise signal is stationary or not;
• when the noise signal is identified as non-stationary, comparing the value of the frequency indicator with a second threshold (TH2), and determining according to the result of this comparison that the noise signal belongs to a first class or a second class of background noise;
• when the noise signal is identified as stationary, comparing the value of the frequency indicator with a third threshold (TH3), and determining according to the result of this comparison that the noise signal belongs to a third class or a fourth class background noise.

• bruit intelligible ;
• bruit d'environnement ;
• bruit de souffle ;
• bruit de grésillement.

Moreover, in this embodiment, the set of classes obtained according to the invention comprises at least the following classes:

• intelligible noise;
• environmental noise;
• breath noise;
• sizzling noise.

The use of the three thresholds TH1, TH2, TH3 above, in a simple tree classification structure makes it possible to quickly classify a noise signal sample. On the other hand, by calculating the class of a sample on windows of short duration, it is possible to obtain a real-time update of the noise background class of the analyzed noise signal.

• des moyens de classification du bruit de fond contenu dans le signal de parole selon un ensemble prédéfini de classes de bruits de fond ;
• des moyens d'évaluation de la qualité vocale du signal de parole, en fonction d'au moins la classification obtenue relative au bruit de fond présent dans le signal de parole.

Correlatively, according to a second aspect, the invention relates to a device for objective evaluation of the voice quality of a speech signal. According to the invention, this device comprises:

• means for classifying the background noise contained in the speech signal according to a predefined set of background noise classes;
• means for evaluating the speech quality of the speech signal, as a function of at least the classification obtained relating to the background noise present in the speech signal.

• un module d'extraction à partir du signal de parole d'un signal de bruit de fond, dit signal de bruit ;
• un module de calcul de paramètres audio du signal de bruit ;
• un module de classification du bruit de fond contenu dans le signal de bruit, en fonction des paramètres audio calculés, selon un ensemble prédéfini de classes de bruits de fond ;
• un module d'évaluation de la qualité vocale du signal de parole, en fonction d'au moins la classification obtenue relative au bruit de fond présent dans le signal de parole.

According to particular embodiments of the invention, this objective voice quality evaluation device comprises:

• an extraction module from the speech signal of a background noise signal, said noise signal;
• a module for calculating audio parameters of the noise signal;
• a noise noise classification module contained in the noise signal, based on the calculated audio parameters, according to a predefined set of background noise classes;
• a voice quality evaluation module of the speech signal, based on at least the obtained classification relating to the background noise present in the speech signal.

According to another aspect, the invention relates to a computer program on an information medium, this program comprising instructions adapted to the implementation of a method according to the invention as briefly defined above, when the program is loaded and executed in a computer.

The advantages provided by the objective speech quality evaluation device and the aforesaid computer program are identical to those mentioned above in connection with the objective evaluation method of the voice quality of a speech signal.

• La figure 1, déjà abordée, est une représentation graphique des notes subjectives moyennes données par des auditeurs testeurs à des messages audio contenant divers types de bruits de fond et selon plusieurs niveaux de sonie, conformément à une étude connue de l'état de la technique ;
• La figure 2 représente une fenêtre logicielle affichée sur un écran d'ordinateur montrant l'arbre de sélection obtenu par apprentissage pour définir un modèle de classification de bruits de fond utilisé selon l'invention ;
• Les figures 3a et 3b représentent un organigramme illustrant un procédé d'évaluation objective de la qualité vocale d'un signal de parole, selon un mode de réalisation de l'invention ;
• La figure 4 est un organigramme détaillant l'étape (fig. 3b, S23) d'évaluation de la qualité vocale d'un signal de parole en fonction de la classification du bruit de fond contenu dans le signal de parole ;
• La figure 5 montre graphiquement le résultat de tests subjectifs d'évaluation de la qualité vocale selon l'invention, ainsi que les courbes obtenues par régression logarithmique, qui lient les notes de qualité perçue à la sonie perçue pour des signaux audio correspondant aux classes de bruit de fond définies selon l'invention ;
• La figure 6 montre graphiquement le degré de corrélation existant entre les notes de qualité obtenues lors des tests subjectifs et celles obtenues selon la méthode d'évaluation objective de la qualité, selon la présente invention ;
• La figure 7 représente un schéma fonctionnel d'un dispositif d'évaluation objective de la qualité vocale d'un signal de parole, selon l'invention.

The invention will be better understood with the aid of the detailed description which follows, made with reference to the appended drawings in which:

• The figure 1 , already discussed, is a graphical representation of the average subjective ratings given by test listeners to audio messages containing various types of background noise and at several loudness levels, according to a known prior art study;
• The figure 2 represents a software window displayed on a computer screen showing the selection tree obtained by learning to define a background noise classification model used according to the invention;
• The figures 3a and 3b represent a flowchart illustrating an objective evaluation method of the speech quality of a speech signal, according to an embodiment of the invention;
• The figure 4 is a flowchart detailing the step ( Fig. 3b , S23) for evaluating the speech quality of a speech signal according to the classification of the background noise contained in the speech signal;
• The figure 5 graphically shows the result of subjective voice quality evaluation tests according to the invention, as well as log-log regression curves, which bind the perceived quality ratings to the perceived loudness for audio signals corresponding to the background noise classes. defined according to the invention;
• The figure 6 graphically shows the degree of correlation between the quality scores obtained in the subjective tests and those obtained according to the objective quality evaluation method, according to the present invention;
• The figure 7 represents a block diagram of an objective evaluation device of the voice quality of a speech signal, according to the invention.

The method of objective evaluation of the voice quality of a speech signal according to the invention is remarkable in that it uses the result of the classification phase of the background noise contained in the speech signal, to estimate the voice quality of the signal. The classification phase of the background noise contained in the speech signal is based on the implementation of a previously constructed background noise classification model, the method of construction of which according to the invention is described below. after.

Construction of the background noise classification model

The construction of a noise classification model takes place conventionally in three successive phases. The first phase consists in determining a sound base composed of audio signals containing various background noises, each audio signal being labeled as belonging to a given noise class. Then, during a second phase is extracted from each sound sample of the base a number of predefined characteristic parameters forming a set of indicators. Finally, during the third phase, called the learning phase, the set of compound pairs, each, of the set of indicators and the associated noise class, is provided to a learning engine intended to provide a classification model for classifying any sound sample on the basis of specific indicators, the latter being selected as the most relevant of the various indicators used during the learning phase. The classification model obtained then makes it possible, based on indicators extracted from any sound sample (not part of the sound database), to provide a noise class to which this sample belongs.

In Document [1] cited above, it is demonstrated that voice quality can be influenced by the meaning of noise in the context of telephony. Thus, if users identify noise as coming from a sound source of the speaker's environment, some indulgence is observed regarding the perceived quality evaluation. Two tests were used to verify this, the first test concerning the interaction of background noise characteristics and sound levels with perceived voice quality, and the second test concerning the interaction of background noise characteristics with the impairments due to noise. voice over IP. Based on the results of the study described in the aforementioned document, the inventors of the present invention, have sought to define parameters (indicators) of an audio signal for measuring and quantifying the meaning of the background noise present in this signal and then to define a method of statistical classification of the background noise according to the selected indicators .

Phase 1- Building a sound base of audio signals

For the construction of the classification model of the present invention, the sound base used consists, on the one hand, of the audio signals used for the subjective tests described in Document [1], and on the other hand of audio signals originating from public sound bases.

With regard to the audio signals from the aforementioned subjective tests, in the first test (see Document [1], section 3.2), 152 sound samples are used. These samples are obtained from eight sentences of the same duration (8 seconds) selected from a standardized list of double sentences, produced by four speakers (two men and two women). These sentences are then mixed with six types of background noise (detailed below) at three different levels of loudness (/ oudness in English). Phrases without background noise are also included. Then all the samples are encoded with a G.711 codec. The results of this first test are illustrated by the figure 1 described above.

In the second test (see Document [1], section 4.1), the same sentences are mixed with the six types of background noise with an average loudness level, then four types of impairments due to voice over IP transmission are introduced. (G.711 codec with 0% and 3% packet loss, G.729 codec with 0% and 3% packet loss). A total of 192 sound samples are obtained according to the second test.

• un bruit rose (pink-noise), considéré comme la référence (bruit stationnaire avec -3 dB/octave de contenu fréquentiel) ;
• un bruit de parole stationnaire (BPS) c'est-à-dire un bruit aléatoire avec un contenu fréquentiel similaire à la voix humaine standardisée (stationnaire) ;
• un bruit électrique, c'est-à-dire un son harmonique ayant une fréquence fondamentale de 50Hz simulant un bruit de circuit (stationnaire) ;
• un bruit d'environnement de ville avec présence de voitures, avertisseurs sonores, etc. (non-stationnaire) ;
• un bruit d'environnement de restaurant avec présence de murmures, bruit de verres, rires, etc. (non-stationnaire) ;
• un son de voix intelligible enregistrée depuis une source TV (non-stationnaire).

The six types of background noise used in the aforementioned subjective tests are:

• a pink noise (pink-noise), considered as the reference (stationary noise with -3 dB / octave of frequency content);
• a stationary speech noise (BPS), that is to say a random noise with a frequency content similar to the standardized human voice (stationary);
• an electrical noise, that is to say a harmonic sound having a fundamental frequency of 50Hz simulating a circuit noise (stationary);
• a city environment noise with the presence of cars, horns, etc. (non-stationary);
• a restaurant environment noise with the presence of whispers, the sound of glasses, laughter, etc. (non-stationary);
• an intelligible voice sound recorded from a TV source (non-stationary).

All sounds are sampled at 8 kHz (16 bits), and an Intermediate System ( IRS) bandpass filter is used to simulate a real telephone network. The six types of noise mentioned above are repeated with degradations related to G.711 and G.729 coding, with packet loss, as well as with several levels of broadcast.

Regarding the audio signals from public sound bases, used to complete the sound base, there are 48 other audio signals, with different noises, such as line noise, wind, car, vacuum, hair dryers, murmurs confused ( babble in English), sounds from the natural environment (bird, running water, rain, etc.), music.

These noises were then subjected to six degradation conditions, as explained below.

Each noise is sampled at 8 kHz, filtered with the IRS8 tool, coded and decoded in G.711 as well as in G.729 in the case of the narrow band (300 - 3400 Hz), then each sound is sampled at 16 kHz, then filtered with the tool described in ITU-T Recommendation P.341 ( " Transmission characteristics for wideband (150-7000 Hz) digital hands-free telephony terminals ", 1998 ), and finally coded and decoded in G.722 (broadband 50 - 7000 Hz). These three degraded conditions are then restored according to two levels whose signal-to-noise ratio (SNR) is respectively 16 and 32. Each noise lasts four seconds. Finally, a total of 288 different audio signals are obtained.

Thus, the sound base used to develop the classification model finally consists of 632 audio signals.

Each sound sample of the sound database is manually tagged to identify a background class of membership. The classes chosen were defined following the subjective tests mentioned in the Document [1] and more precisely, were determined according to the indulgence vis-à-vis the perceived noise, manifested by the human subjects tested during the judgment of the voice quality depending on the type of background noise (among the 6 types mentioned above).

• Classe 1 : BDF "intelligible" - il s'agit de bruit de nature intelligible tels que de la musique, de la parole, etc. Cette classe de bruit de fond provoque une forte indulgence sur le jugement de la qualité vocale perçue, par rapport à un bruit de souffle de même niveau.
• Classe 2 : BDF "d'environnement" - il s'agit de bruits ayant du contenu informationnel et fournissant des informations sur l'environnement du locuteur, comme des bruits de ville, de restaurant, de nature, etc. Cette classe de bruit provoque une légère indulgence sur le jugement de la qualité vocale perçue par les utilisateurs par rapport à un bruit de souffle de même niveau.
• Classe 3 : BDF "souffle" - Ces bruits sont de nature stationnaire et ne contiennent pas de contenu informationnel, il s'agit par exemple de bruit rose, de bruit de vent stationnaire, de bruit de parole stationnaire (BPS).
• Classe 4 : BDF "grésillement" - il s'agit de bruits ne contenant pas de contenu informationnel, comme du bruit électrique, du bruit non stationnaire bruité, etc. Cette classe de bruit provoque une forte dégradation de la qualité vocale perçue par les utilisateurs, par rapport à un bruit de souffle de même niveau.

Thus, four classes of background noise (BDF) were selected:

• Class 1: BDF "intelligible" - this is noise of intelligible nature such as music, speech, etc. This class of background noise causes a strong indulgence on the judgment of the perceived vocal quality, compared to a sound of the same level.
• Class 2: BDF "environment" - These are noises with informational content and information about the speaker's environment, such as city noise, restaurant noise, nature noise, etc. This class of noise causes a slight indulgence on the judgment of the voice quality perceived by the users compared to a noise of the same level.
• Class 3: BDF "breath" - These noises are stationary in nature and do not contain informational content, such as pink noise, stationary wind noise, stationary speech noise (BPS).
• Class 4: BDF "sizzling" - noise that does not contain informational content, such as electrical noise, noisy noisy noise, etc. This class of noise causes a strong degradation of the voice quality perceived by the users, compared to a blast noise of the same level.

Phase 2 - Extraction of parameters of audio signals from the sound base

• (1) La corrélation du signal : il s'agit d'un indicateur utilisant le coefficient de corrélation de Bravais-Pearson appliqué entre le signal entier et le même signal décalé d'un échantillon numérique.
• (2) Le taux de passage par zéro (ZCR) du signal ;
• (3) La variation du niveau acoustique du signal ;
• (4) Le centre de gravité spectral (Spectral Centroid) du signal ;
• (5) La rugosité spectrale du signal ;
• (6) Le flux spectral du signal ;
• (7) Le point spectral de coupure (Spectral Rolloff Point) du signal ;
• (8) Le coefficient harmonique du signal.

For each of the audio signals of the sound database, eight parameters or indicators known per se are calculated. These indicators are as follows:

• (1) Signal correlation: This is an indicator using the Bravais-Pearson correlation coefficient applied between the entire signal and the same shifted signal of a digital sample.
• (2) Zero crossing rate (ZCR) of the signal;
• (3) The variation of the sound level of the signal;
• (4) The spectral center of gravity ( Spectral Centroid) of the signal;
• (5) the spectral roughness of the signal;
• (6) the spectral flux of the signal;
• (7) Spectral Rolloff Point of the signal;
• (8) The harmonic coefficient of the signal.

Phase 3 - Obtaining the classification model

The classification model is obtained by learning using a decision tree (cf. figure 1 ), carried out using the statistical tool called "classregtree" of the MATLAB® environment marketed by The MathWorks. The algorithm used is developed from techniques described in the book entitled " Classification and regression trees "by Leo Breiman et al., Published by Chapman and Hall in 1993 .

Each sample of background noise from the sound database is indicated by the eight indicators mentioned above and the class of membership of the sample (1: intelligible, 2: environment, 3: breath, 4: sizzle). The decision tree then calculates the various possible solutions in order to obtain an optimum classification, closest to the manually labeled classes. During this learning phase, the most relevant audio indicators are selected, and value thresholds associated with these indicators are defined, these thresholds making it possible to separate the different classes and subclasses of background noise.

During learning, 500 backgrounds of different types are randomly selected from the 632 of the sound base. The result of the classification obtained by apprenticeship is represented in figure 1 .

As can be seen in the decision tree shown in figure 2 , the resulting classification uses only two of the original eight indicators to rank the 500 background noises of learning in the four classes predefined. The indicators selected are the indicators (3) and (6) of the list introduced above and respectively represent the variation of the acoustic level and the spectral flow of the background noise signals.

As represented in figure 2 , the classification model obtained by learning begins by separating the background noise according to their stationarity character. This stationarity character is highlighted by the temporal indicator characteristic of the variation of the acoustic level (indicator (3)). Thus, if this indicator has a value lower than a first threshold - TH1 = 1.03485 - then the background noise is considered as stationary (left branch), otherwise the background noise is considered as non-stationary (right branch). Then, the characteristic frequency indicator of the spectral flow (indicator (6)) in turn filters each of the two categories (stationary / non-stationary) selected with the indicator (3).

Thus, when the noise signal is considered non-stationary, if the frequency indicator is less than a second threshold - TH2 = 0.280607 - then the noise signal belongs to the "environment" class, otherwise the noise signal belongs to the class "intelligible". On the other hand, when the noise signal is considered stationary, if the frequency indicator (indicator (6), spectral flux) is less than a third threshold - TH3 = 0.145702 - then the noise signal belongs to the class "sizzle", otherwise the noise signal belongs to the class "breath".

• 100% pour la classe "grésillement",
• 96,4% pour la classe "souffle",
• 79,2% pour la classe "environnement",
• 95,9% pour la classe "intelligible".

The selection tree ( fig.1 ), obtained with the two aforementioned indicators, correctly classified 86.2% of the background noise signals among the 500 audio signals subjected to learning. More specifically, the good classification proportions obtained for each class are as follows:

• 100% for the class "sizzling",
• 96.4% for the "breath" class,
• 79.2% for the "environment" class,
• 95.9% for the class "intelligible".

It can be noted that the "environment" class gets a lower classification result than for the other classes. This result is due to the differentiation between "breath" and "environmental" sounds, which can sometimes be difficult to perform, because of the similarity of certain sounds that can be arranged in both classes, for example sounds such as wind noise or the sound of a hair dryer.

The indicators selected for the classification model according to the invention are defined in greater detail below.

The time indicator , designated in the rest of the description by "IND_TMP", is characteristic of the variation of the sound level of any noise signal is defined by the standard deviation of the power values of all the considered frames of the signal. In a first step, a power value is determined for each of the frames. Each frame is composed of 512 samples, with overlapping between successive frames of 256 samples. For a sampling frequency of 8000 Hz, this corresponds to a duration of 64 ms (milliseconds) per frame, with an overlap of 32 ms. This overlap is used by 50% to obtain continuity between successive frames, as defined in Document [5] : " P.56 Objective Measurement of Active Voice Level ', ITU-T Recommendation, 1993 .

When the noise to be classified has a length greater than one frame, the sound power value for each of the frames may be defined by the following mathematical formula: $$P\left(\mathit{weft}\right)=10\mathrm{<figure-callout\; id="1"\; label="log"\; filenames="imgf0001.png,imgf0005.png"\; state="\{\{state\}\}">log</figure-callout>}\left(\frac{1}{{\mathrm{The}}_{\mathit{weft}}}{\displaystyle \underset{i=1}{\overset{{\mathrm{The}}_{\mathit{<figure-callout\; id="2"\; label="weft\; x\; i"\; filenames="imgf0001.png,imgf0003.png"\; state="\{\{state\}\}">weft\; </figure-callout>}}}{\Sigma }}}{x}_i^{}{}_2^{}\right)$$

Where: "frame" means the number of the frame to be evaluated; " The _frame " refers to the length of the frame (512 samples); " x _i " corresponds to the amplitude of the sample i ; "log" refers to the decimal logarithm. This calculates the logarithm of the calculated average to obtain a power value per frame.

The value of the time indicator "IND_TMP" of the considered background noise is then defined by the standard deviation of all the power values obtained, by the following relation: $$\mathrm{IND\_TMP}=\sqrt{\frac{1}{\mathit{Ntrame}}{\displaystyle \underset{i=1}{\overset{\mathit{Ntrame}}{\Sigma }}{\left(P_i-<P>\right)}^2}}$$

Where: N _frame represents the number of frames present in the background noise considered; P _i represents the power value for the frame i ; and < P > is the average power on all frames.

According to the time indicator IND_TMP, the more a sound is non-stationary and the higher the value obtained for this indicator.

The frequency indicator , designated in the rest of the description by "IND_FRQ" and characteristic of the spectral flux of the noise signal, is calculated from the Spectral Power Density (DSP) of the signal. The DSP of a signal - derived from the Fourrier transform of the autocorrelation function of the signal - makes it possible to characterize the spectral envelope of the signal, in order to obtain information on the frequency content of the signal at a given moment, such as for example formants, harmonics, etc. According to the embodiment presented, this indicator is determined by frame of 256 samples, corresponding to a duration of 32 ms for a sampling frequency of 8 KHz. There is no frame overlap, unlike the time indicator.

Spectral flow (SF), also referred to as "spectrum amplitude variation," is a measure of the rate of change of a power spectrum of a signal over time. This indicator is calculated from the normalized cross-correlation between two successive amplitudes of the spectrum a _k (t-1) and a _k (t). The spectral flow (SF) can be defined by the following mathematical formula: $$\mathit{SF}\left(\mathit{weft}\right)=1-\frac{{\mathrm{\Sigma }}_k{\mathrm{at}}_k\left(t-1\right)\mathrm{.}{\mathrm{at}}_k\left(t\right)}{\sqrt{{\mathrm{\Sigma }}_k{\mathrm{at}}_k{\left(t-1\right)}^2}\sqrt{{\mathrm{\Sigma }}_k{\mathrm{at}}_k{\left(\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="imgf0001.png,imgf0003.png"\; state="\{\{state\}\}">t</figure-callout>}\right)}^2}}$$

Where: " k " is an index representing the different frequency components, and " t " is an index representing successive frames without overlapping, consisting of 256 samples each.

In other words, a value of the spectral flux (SF) corresponds to the amplitude difference of the spectral vector between two successive frames. This value is close to zero if the successive spectra are similar, and is close to 1 for very different successive spectra. The value of the spectral stream is high for a music signal because a musical signal varies greatly from one frame to another. For speech, with the alternation of periods of stability (vowel) and transitions (consonant / vowel), the measurement of the spectral flow takes very different values and varies strongly during a sentence.

When the noise to be classified has a length greater than one frame, the final expression used for the frequency indicator is defined as the average of the spectral flux values for all the frames of the signal, as defined in the equation below: $$\mathit{IND\_FRQ}=\frac{1}{\mathit{Ntrame}}{\displaystyle \underset{i=1}{\overset{\mathit{Ntrame}}{\Sigma }}}\mathit{SF}\left(i\right)$$

Using the background noise classification model

The classification model of the invention, obtained as explained above, is used according to the invention to determine, on the basis of indicators extracted from any noisy audio signal, the noise class to which this noisy signal belongs among the noise level. set of classes defined for the classification model.

The figures 3a and 3b represent a flowchart illustrating a method of objective evaluation of the voice quality of a speech signal, according to an embodiment of the invention. According to the invention, the method of classification of background noise is implemented prior to the actual phase of evaluation of voice quality.

As represented in figure 3a the first step S1 is to obtain an audio signal, which in the embodiment presented here is a speech signal obtained in analog or digital form. In this embodiment, as illustrated by step S3, a voice activity detection (DAV) operation is then applied to the speech signal. The purpose of this voice activity detection is to separate in the input audio signal the periods of the speech-containing signal, possibly noisy, periods of the signal containing no speech (periods of silence), therefore not being able to contain only noise. Thus, during this step, the active areas of the signal, that is to say presenting the noisy voice message, are separated from each other. inactive areas noisy. In practice, in this embodiment, the voice activity detection technique implemented is that described in Document [5] cited above (" P.56 Objective Measurement of Active Voice Level ", ITU-T Recommendation, 1993 ).

• détecter l'enveloppe du signal,
• comparer l'enveloppe du signal avec un seuil fixe en prenant en compte un temps de maintien de la parole,
• déterminer les trames de signal dont l'enveloppe est située au dessus du seuil (DAV=1 pour les trames actives) et en dessous (DAV=0 pour le bruit de fond). Ce seuil est fixé à 15,9 dB (décibel) en dessous du niveau vocal actif moyen (puissance du signal sur les trames actives).

In summary, the principle of the DAV technique used consists of:

• detect the signal envelope,
• compare the envelope of the signal with a fixed threshold by taking into account a time of maintenance of the speech,
• determine the signal frames whose envelope is above the threshold (DAV = 1 for active frames) and below (DAV = 0 for background noise). This threshold is set at 15.9 dB (decibel) below the average active voice level (signal strength on active frames).

Once voice detection is performed on the audio signal, the background noise signal generated (step S5) is the signal consisting of the periods of the audio signal for which the result of the speech activity detection is zero.

Once the noise signal has been generated, the audio parameters consisting of the two indicators mentioned above (time indicator IND_TMP and frequency indicator IND_FRQ), which were selected during the obtaining of the classification model (learning phase), are extracted. of the noise signal, in step S7.

Then the tests S9, S11 ( Fig. 3a ) and S17 ( Fig. 3b ) and associated decision branches, correspond to the decision tree described above in relation to the figure 2 . Thus, in step S9 the value of the time indicator (IND_TMP) obtained for the noise signal is compared with the first threshold TH1 mentioned above. If the value of the time indicator is greater than the threshold TH1 (S9, no) then the noise signal is of non-stationary type and then the test of step S11 is applied.

During the test S11 the frequency indicator (IND_FRQ) this time, is compared to the second threshold TH2 mentioned above. If the indicator IND_FRQ is greater (S11, no) than the threshold TH2, the class (CL) of the noise signal is determined (step S13) as CL1: "Noise intelligible"; otherwise the class of the noise signal is determined (step S15) as CL2: "Noise The classification of the analyzed noise signal is then completed and the evaluation of the voice quality of the speech signal can then be performed ( Fig. 3b step S23).

During the initial test S9, if the value of the time indicator is below the threshold TH1 (S9, yes) then the noise signal is of stationary type and then the test of step S17 is applied ( Fig. 3b ). In test S17, the value of the frequency indicator IND_FRQ is compared with the third threshold TH3 (defined above). If the indicator IND_FRQ is greater (S17, no) than the threshold TH3, the class (CL) of the noise signal is determined (step S19) as being CL3: "Breath noise"; otherwise the class of the noise signal is determined (step S21) as being CL4: "Sizzling noise". The classification of the analyzed noise signal is then completed and the voice quality evaluation of the speech signal can then be performed ( Fig. 3b step S23).

The figure 4 detail the step ( Fig. 3b , S23) for evaluating the speech quality of a speech signal according to the classification of the background noise contained in the speech signal. As represented in figure 4 , the voice quality evaluation operation starts with step S231 in which, the total loudness of the noise signal ( SIG_N ) is estimated.

It is recalled here that the loudness is defined as the subjective intensity of a sound, it is expressed in sones or phones. The total loudness measured subjectively (perceived loudness), however, can be estimated using models known targets such as Zwicker model or model of Moore.

The Zwicker model is described for example in the document entitled " Psychoacoustics: Facts and Models "by E. Zwicker and H. Fastl - Berlin, Springer, 2nd updated edition, 14 April 1999 .

Moore's model is described for example in the document: " A Model for the Prediction of Thresholds, Loudness, and Partial Loudness, by BCJ Moore, BR Glasberg and T. Baer - Journal of the Audio Engineering Society 45 (4): 224-240, 1997 .

In the context of the embodiment set forth herein, the total loudness of the noise signal is estimated using the Zwicker model, however also implement the invention using the Moore model. Moreover, the more accurate the loudness estimation model used, the more precise the voice quality evaluation according to the invention will be.

The total loudness estimate, expressed in sones, of the noise signal SIG_N, obtained using the Zwicker model, is referred to herein as " N ". Thus at the end of step S231 shown in figure 4 , we obtain an estimate of the loudness of the noise signal.

The following step S233 is the actual evaluation step of the voice quality of the speech signal. According to the method, one starts by selecting a mathematical formula to be used, out of four, as a function of the noise class CLi (i = 1, 2, 3, 4) obtained during the preliminary phase of classification of the background noise ( obtaining the above formulas is detailed below).

The general expression of the selected formula is: $$\mathit{MOS\_CLi}={\mathrm{VS}}_{i-1}+{\mathrm{VS}}_i\times f\left(\mathrm{NOT}\right)$$

• ■ MOS_CLi est la note calculée pour le signal de bruit SIG_N de classe CLi ;
• ■ f(N) est une fonction mathématique de la sonie totale, N, estimée pour le signal de bruit, selon un modèle de sonie tel que le modèle de Zwicker ;
• ■ C_i-1 et C_i sont deux coefficients définis pour la formule mathématique associée à la classe CLi.

Or:

• ■ MOS_CLi is the calculated score for the class CLi SIG_N noise signal ;
• ■ f (N) is a mathematical function of the total loudness, N , estimated for the noise signal, according to a loudness model such as the Zwicker model;
• ■ C _i-1 and C _i are two coefficients defined for the mathematical formula associated with the CLi class .

The mathematical expression of formula (5) above demonstrates the fact that, according to the invention, there is available a voice quality evaluation model for each class of background noise (CL1- CL4), which is a function of the total loudness of the background noise.

Thus, in the embodiment described here, the voice quality score for the speech signal, MOS_CLi, is obtained, on the one hand, as a function of the classification obtained relating to the background noise present in the speech signal - by the choice of the coefficients ( C _i-1 ; C _i ) of the mathematical formula which correspond to the background noise class - and on the other hand, according to the estimated loudness N for the background noise.

Obtaining speech quality assessment models by background noise class

We will now detail how to obtain voice quality evaluation models for each class of background noise (CL1-CL4). The figure 1 described above, resulting from the aforementioned Document [1] , represents the opinion means (MOS LQSN) with the associated confidence interval, calculated from notes given by auditors to audio messages containing six types of noise. different backgrounds, according to the ACR (Absolute Category Rating ) method. The various types of noise are: pink noise, stationary speech noise (BPS), electrical noise, city noise, restaurant noise, television noise or voice, each noise being considered at three different levels of perceived loudness. The loudness levels of the various types of background noise are obtained in this test, subjectively.

More specifically, the sound base used in the first test described in Document [1] (see section 2 of the document) consists of eight sentences, half of which are pronounced by two men and the other half by two women. Each of these pronounced sentences constitutes a speech signal (8 speech signals). Then, to each of these speech signals is added each of the six aforementioned background noise, then 48 noisy speech signals (8 signals per type of background noise) are obtained. During the test, each of these noisy speech signals is presented to the listening auditors in three levels of different isosony, which constitutes 144 different noisy signals. On the other hand, to each of the 8 initial speech signals (pronounced sentence) is added pink background noise (SNR = 44), to represent the condition corresponding to a speech signal without background noise. In all, 152 speech signals were used in the first test.

As regards the levels of isosony used, these were determined in advance according to the "Adjustment test" of the first test described in Document [1] (Section 2). in accordance with the results described in the document entitled " The sound of impulse sounds: Perception, Measures and Models ", thesis of Isabelle Boullet - University of Aix-Marseille 2, 2005 . In short, this test consists of asking people to change the level of each noise signal so that the loudness of the signal is equal to the loudness of the reference signal which is the pink noise. In practice the three loudness levels (expressed in sones) determined for each of the six types of background noise used are the following: 4.6 sone; 8.2 sone; 14 sone. The loudness level of each of the reference speech signals, without background noise (i.e. containing only pink noise with SNR = 44) is 1.67 sone.

• la classe 1 (CL1 : "intelligible") correspond aux bruits de TV/parole ;
• la classe 2 (CL2 : "environnement") correspond au regroupement des bruits de ville et bruits de restaurant ;
• la classe 3 (CL3 : "souffle") regroupe le bruit rose et le bruit de parole stationnaire (BPS) ; et
• la classe 4 (CL4 : "grésillement") correspond aux bruits électriques.

From the results of the test illustrated by the figure 1 , the six types of background noise used made it possible to define the four classes of background noise used according to the invention, as follows:

• Class 1 (CL1: "intelligible") corresponds to TV / speech noises;
• class 2 (CL2: "environment") is the combination of city noise and restaurant noise;
• Class 3 (CL3: "breath") includes pink noise and stationary speech noise (BPS); and
• Class 4 (CL4: "sizzling") corresponds to electrical noise.

Thus, each test audio signal can be characterized by its background noise class (CL1-CL4), its perceived loudness level (in sones: 1.67, 4.6, 8.2, 14) and the MOS note. -LQSN (Listening Quality Subjective Narrowband ) assigned to him in the preliminary subjective test (Document [1], "Préliminary Experiment "). Therefore, in summary, in this test, 24 subjects underwent an assessment of the overall quality of audio signals, according to the ACR method. Finally, 152 MOS-LQSN scores were obtained by taking the average score given by the 24 subjects, for each of the 152 audio test signals, which are divided according to the four classes of background noise defined according to the invention.

The figure 5 graphically shows the result of the aforementioned subjective tests. The 152 test conditions are represented by their points, each corresponding point on the abscissa, at a loudness level, and on the ordinate, at the assigned quality score (MOS-LQSN); the points are furthermore differentiated according to the class of the background noise contained in the corresponding audio signal.

According to the invention, starting from the point clouds resulting from the subjective tests, the modeling of the evaluation of the vocal quality by class of noise, was carried out by mathematical regression. In practice several types of regression have been tested (polynomial regression, linear), but it is the logarithmic regression as a function of the perceived loudness, expressed in sones, which makes it possible to obtain the best correlations with the notes of perceived vocal quality.

To the figure 5 Logarithmic regression curves can be observed which relate the perceived quality scores to the perceived loudness, expressed in sones, for audio signals corresponding to the background noise classes defined according to the invention. The figure 5 also indicates the equations obtained for each of the four curves obtained by logarithmic regression. Thus the first equation at the top right corresponds to class 1, the second to class 2, the third to class 3, and the fourth to class 4.

For each of these equations, the value associated with R ² corresponds to the correlation coefficient between the results obtained from the subjective test and the corresponding logarithmic regression.

Thus the equation (5) explained above is declined, in practice, for the different classes as follows: $$\mathit{MOS\_CLi}={\mathrm{VS}}_{i-1}+{\mathrm{VS}}_i\times \ln \left(\mathrm{NOT}\right)$$

• Ln(N) : logarithme népérien de la valeur de sonie totale, N, calculée et exprimée en sones ;
• (C_i-1 ; C_i ) = (4,4554 ; - 0,5888) pour i=1 (classe 1) ;
• (C_i-1; C_i ) = (4,7046 ; - 0,7869) pour i=2 (classe 2) ;
• (C_i-1 ; C_i ) = (4,9015 ; - 0,9592) pour i=3 (classe 3) ;
• (C_i-1; C_i ) = (4,7489 ; - 0,9608) pour i=4 (classe 4) ;

With:

• Ln (N) : natural logarithm of the total loudness value, N , calculated and expressed in sones;
• ( C _i-1 ; C _i ) = (4.4554; - 0.5888) for i = 1 (class 1);
• ( C _i-1 ; C _i ) = (4,7046; - 0,7869) for i = 2 (class 2);
• ( C _i-1 ; C _i ) = (4.9015; 0.9592) for i = 3 (class 3);
• ( C _i-1 ; C _i ) = (4.7489; -0.9608) for i = 4 (class 4);

In the context of the objective voice quality evaluation model according to the invention, the perceived loudness value N - subjectively obtained value in the context of the aforementioned subjective tests - is obtained by estimation according to a known method of loudness estimation, the Zwicker model in the embodiment set forth herein.

The figure 6 graphically shows the degree of correlation between the quality scores obtained in the subjective tests and those obtained using the objective quality evaluation method, according to the present invention. As can be seen on the figure 6 a very good correlation, of the order of 93% (r = 0.93205), is obtained between the MOS-LQSN scores resulting from the subjective test explained above (x-axis), and the objective MOS scores (axes of the ordinates) obtained with the quality evaluation model according to the invention, as defined by equation (6) above.

In connection with the figure 7 a device for objective evaluation of the speech quality of a speech signal according to the invention will now be functionally described. This voice quality evaluation device is designed to implement the voice quality evaluation method according to the invention which has just been described above.

As represented in figure 7 , the device 1 for evaluating the voice quality of a speech signal, comprises a module 11 for extracting from the audio signal (SIG) of a background noise signal (SIG_N), said noise signal.

The speech signal (GIS) input to the voice quality evaluation device 1 can be delivered to the device 1 from a communication network 2, such as a voice over IP network for example.

According to the embodiment shown, the module 11 is in practice a voice activity detection module. The module DAV 11 then provides a noise signal SIG_N which is inputted to a module 13 for extracting parameters, that is to say calculating the parameters constituted by the time and frequency indicators, respectively IND_TMP and IND_FRQ. The calculated indicators are then provided to a classification module, implementing the classification model according to the invention, described above, which determines, as a function of the values of the indicators used, the background noise class (CL) to which the noise signal SIG_N, according to the algorithm described in connection with the figures 3a and 3b .

The result of the classification performed by the background noise classification module 15 is then provided to voice quality evaluation module 17. The latter implements the voice quality evaluation algorithm described above in connection with the figure 4 to ultimately deliver an objective speech quality score relating to the input speech signal (SIG).

In practice, the voice quality evaluation device according to the invention is implemented in the form of software means, that is to say computer program modules, performing the functions described in connection with the figures 3a , 3b , 4 and 5 .

Moreover, in the context of a particular implementation of the invention, the voice quality evaluation module 17 can be incorporated in a computer machine separate from that housing the other modules. In particular, the background noise class information (CL) can be routed via a communication network to the machine or server responsible for performing the voice quality evaluation. Furthermore, according to a particular application of the invention, in the field for example of the supervision of the voice quality on a communication network, each voice quality score calculated by the module 17 is sent to a local collection equipment or on the network, responsible for collecting this quality information in order to establish an overall quality score, established for example as a function of time and / or according to the type of communication and / or according to other types of quality notes .

The aforementioned program modules are implemented when they are loaded and executed in a computer or computer device. Such a computing device may also be constituted by any processor system integrated in a communication terminal or in a communication network equipment.

It will also be noted that a computer program according to the invention, the purpose of which is the implementation of the invention when it is executed by an appropriate computer system, can be stored on an information carrier of various types. . Indeed, such an information carrier may be constituted by any entity or device capable of storing a program according to the invention.

For example, the medium in question may comprise a hardware storage means, such as a memory, for example a CD ROM or a ROM or RAM microelectronic circuit memory, or a magnetic recording means, for example a Hard disk.

From a design point of view, a computer program according to the invention can use any programming language and be in the form of source code, object code, or intermediate code between source code and object code (for example eg, a partially compiled form), or in any other form desirable for implementing a method according to the invention.

Claims (15)

1. Method for objective evaluation of the voice quality of a speech signal, characterized in that it comprises the steps for:

   - classification (S3-S21) of the background noises contained in the speech signal according to a predefined set of classes of background noises (CL1-CL4);

   - evaluation (S23) of the voice quality of the speech signal, according to at least the classification obtained relating to the background noises present in the speech signal.
2. Method according to Claim 1, in which the step for classification of the background noises contained in the speech signal includes the steps for:

   - extraction (S3, S5) from the speech signal of a background noise signal, referred to as noise signal;

   - calculation (S7) of audio parameters of the noise signal;

   - classification (S9-S21) of the background noises contained in the noise signal as a function of the calculated audio parameters, according to said set of classes of background noise (CL1-CL4).
3. Method according to Claim 2, in which the step (S23) for evaluation of the voice quality of the speech signal comprises the steps for:

   - estimation (S231) of the total loudness (N) of the noise signal (SIG_N);

   - calculation of a voice quality score (MOS_CLi) as a function of the class (CLi) of background noise present in the speech signal, and of the total loudness (N) estimated for the noise signal.
4. Method according to Claim 3, in which a voice quality score (MOS_CLi) is obtained according to a mathematical formula of the following general form: $$\mathit{MOS\_CLi}=C_{i-1}+C_i\times f\left(N\right)$$

   

   
   where:

   ■ MOS_CLi is the score calculated for the noise signal;

   ■ f(N) is a mathematical function of the total loudness, N, estimated for the noise signal;

   ■ C_i-1 and C_i are two coefficients defined for the class (CLi) of background noise obtained for the noise signal.
5. Method according to Claim 4, in which the function f(N) is the natural logarithm, Ln(N), of the total loudness N expressed in sones.
6. Method according to one of Claims 3 to 5, in which the total loudness of the noise signal is estimated according to an objective model for estimation of the loudness.
7. Method according to any one of Claims 2 to 6, in which the step (S7) for calculation of audio parameters of the noise signal comprises the calculation of a first parameter (IND_TMP), referred to as time indicator, relating to the time variation of the noise signal, and of a second parameter (IND_FRQ), referred to as frequency indicator, relating to the frequency spectrum of the noise signal.
8. Method according to Claim 7, in which the time indicator (IND_TMP) is obtained from a calculation of variation of the sound level of the noise signal, and the frequency indicator (IND_FRQ) is obtained from a calculation of variation of the amplitude of the frequency spectrum of the noise signal.
9. Method according to any one of the preceding claims, in which, in order to classify the background noises associated with the noise signal, the method comprises the steps consisting in:

   - comparing (S9) the value of the time indicator (IND_TMP) obtained for the noise signal with a first threshold (TH1) and determining, depending on the result of this comparison, whether the noise signal is stationary or not;

   - when the noise signal is identified as non-stationary, comparing (S11) the value of the frequency indicator with a second threshold (TH2) and determining (S13, S15), depending on the result of this comparison, whether the noise signal belongs to a first class (CL1) or to a second class (CL2) of background noise;

   - when the noise signal is identified as stationary, comparing (S17) the value of the frequency indicator with a third threshold (TH3) and determining (S19, S21), depending on the result of this comparison, whether the noise signal belongs to a third class (CL3) or to a fourth class (CL4) of background noise.
10. Method according to any one of the preceding claims, in which the set of classes comprises at least the following classes:

    - intelligible noise;

    - environmental noise;

    - blowing noise;

    - crackling noise.
11. Method according to any one of Claims 2 to 10, in which the noise signal is extracted by application to the speech signal of an operation for detection of voice activity, the regions of the speech signal not exhibiting voice activity constituting the noise signal.
12. Device for objective evaluation of the voice quality of a speech signal, characterized in that it comprises:

    - means of classification (11-15) of the background noises contained in the speech signal according to a predefined set of classes of background noise (CL1-CL4);

    - means of evaluation (17) of the voice quality of the speech signal as a function of at least the classification obtained relating to the background noises present in the speech signal.
13. Device according to Claim 12, comprising:

    - a module (11) for extraction from the speech signal (SIG) of a background noise signal, referred to as noise signal;

    - a module (13) for calculation of audio parameters of the noise signal;

    - a module (15) for classification of the background noises contained in the noise signal as a function of the calculated audio parameters, according to a predefined set of classes of background noise (CL);

    - a module (17) for evaluation of the voice quality of the speech signal as a function of at least the classification obtained relating to the background noises present in the speech signal.
14. Device according to Claim 13, furthermore comprising means designed for the implementation of a method according to any one of Claims 2 to 11.
15. Computer program on information media, said program comprising program instructions designed for the implementation of a method according to any one of Claims 1 to 11, when said program is loaded and executed in a computer.

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