EP1849157A1

EP1849157A1 - Method of measuring annoyance caused by noise in an audio signal

Info

Publication number: EP1849157A1
Application number: EP06709505A
Authority: EP
Inventors: Nicolas Le Faucheur; Valérie GAUTIER-TURBIN
Original assignee: France Telecom SA
Current assignee: Orange SA
Priority date: 2005-02-18
Filing date: 2006-02-13
Publication date: 2007-10-31
Anticipated expiration: 2026-02-13
Also published as: US20080267425A1; FR2882458A1; WO2006087490A1; EP1849157B1; DE602006008111D1; ATE438173T1; ES2329932T3

Abstract

A method of computing an objective score (NOB) of annoyance caused by noise in an audio signal processed by a noise reduction function, said method including a preliminary step of obtaining a predefined test audio signal (x[m]) containing a wanted signal free of noise, a noisy signal (xb[m]) obtained by adding a predefined noise signal to said test signal (x[m]), and a processed signal (y[m]) obtained by applying the noise reduction function to said noisy signal (xb[m]), wherein said method further includes a step (a 3, a 4 ) of measuring the apparent loudness of frames of said noisy signal (xb[m]) and said processed signal (y[m]) and of measuring tonality coefficients of frames of said processed signal (y[m]).

Description

Method of measuring noise annoyance in an audio signal

The present invention is generally in the fields of speech signal processing and psychoacoustics. More specifically, the invention relates to a method and a device for objective evaluation of the annoyance due to noise in audio signals.

The invention makes it possible to note objectively the annoyance due to noise in an audio signal processed by a noise reduction function. In the field of audio signal transmission, a noise reduction function, also known as a noise canceling or denoising function, is intended to reduce the background noise level in a voice communication, or having at least one component voice. It has a specific interest when one of the interlocutors of this communication is immersed in a noisy environment that greatly impairs the intelligibility of his voice. The noise reduction algorithms are based on a continuous estimation of the background noise level from the incident signal and a speech activity detection to distinguish the noise periods only from those with the useful speech signal. A filtering of the incident speech signal corresponding to the noisy speech signal is then performed to reduce the noise contribution determined from the noise estimate.

The annoyance due to the presence of noise in an audio signal processed by such a noise reduction function is subjectively assessed today only on the basis of the exploitation of test results implemented according to the document "Recommendation ITU -T P.835 (11/2003) ", This assessment is made on a MOS scale, according to the English Mean Opinion Score, which gives a score of one to five of the annoyance due to noise, called" background noise "in this same document. The major disadvantage of this evaluation technique is the need to implement subjective tests, this implementation being very heavy and very expensive. Indeed, each particular context, that is to say a type of incident signal associated with a type of noise and a noise reduction function, requires putting a panel of people in situation of actual listening of speech samples. to ask them to note the annoyance due to noise on a MOS scale.

That is why the development of alternative objective methods that can complement or supplement subjective methods is a subject of great interest. The most striking illustration of this phenomenon is the evolving listening quality model contained in ITU-T Recommendation P.862 (02/2001), but this model does not apply to the evaluation of the annoyance due to noise. The invention relates in fact to speech signals in which the annoyance due to noise can be significant, this before or after treatment of these signals by a possible noise reduction function.

It should also be noted that, although in general the invention will be used to evaluate noise annoyance at the output of communication equipment implementing a noise reduction function, the invention also applies to noisy signals. not treated by such a function. The case of use of the invention on any noisy audio signal is therefore a particular case of the more general case of use of the invention on an audio signal processed by a noise reduction function.

The present invention aims to overcome the disadvantages of the prior art by providing a method and an objective computing device of a score equivalent to the subjective score as indicated in the document "ITU-T Recommendation P.835", characterizing the annoyance due to the presence of noise in an audio signal. The method according to the invention varies according to whether the invention is used on any noisy audio signal or on an audio signal processed by a noise reduction function, in particular in the parameters for calculating the objective score according to the invention. In order to describe these two use cases, two embodiments that can also be considered as two distinct processes are presented. However, the second embodiment, applying to any noisy audio signal, and more general than the first embodiment, is easily deduced therefrom.

To this end, the invention proposes a method of calculating an objective note of the annoyance due to noise in an audio signal processed by a noise reduction function, said method comprising a preliminary step of obtaining an audio signal. predefined test device comprising a useful signal devoid of noise, a noisy signal obtained by adding a predefined noise signal to said test signal, and a processed signal obtained by applying the noise reduction function to said noisy signal said method being characterized in that it includes a step of measuring loudness of frames of said noisy signal and said processed signal, and measures of frame pitch coefficients of said processed signal.

This method has the advantage of a simple, immediate and rapid implementation contrary to subjective tests. It will be recalled here that the expression "psychoacoustic sony" can be defined as the character of the auditory sensation related to the sound pressure and the structure of the sound. In other words, it is the sound force of a sound or a sound as an auditory sensation (see Office de la langue française, 1988). The loudness is represented by a psychoacoustic loudness scale (in sones). On the other hand, loudness, still referred to as "subjective intensity," is a special measure of loudness.

According to a preferred characteristic, this method according to the invention comprises the steps of;

- Calculation of mean loudness densities Sr (Vn) of frames of the processed signal, of respective mean loudness densities speech) and s-djn speech) of useful signal frames "m_parole" respectively of noisy signal and processed signal, mean loudness densities ^~ Sr {m_noise) of noise frames "m_noise" of the processed signal, and tone coefficients ar (m noise) of "m_noise" noise frames of the processed signal, - Calculation of an objective note of the annoyance due to the noise in the processed signal, from said average loudness densities and said calculated tone coefficients, and predefined weighting coefficients.

In a preferred embodiment, the step of calculating average loudness densities and tonality coefficients is followed by a step averaging & - &", _pam ie, Sr Pamie, 5V bn, u ar _ and brown said average loudness densities and said tone coefficients over all the relevant frames of the corresponding signals, and the objective noise noise score is calculated according to the following equation: s NOB -] jT ωfactor (i) + o & ,

where fartrurHY- ¹ ^ - * ""^''

_X _{/ n \} Sr noise factor (2) = - = - -

SY_ word factor (3) = Type_offset _ word) - Sr (m _ speech)), the operator

"Deviation_type (v (m))" designating the standard deviation of the variable v over the set of frames of subscript m, factor (4) = ar_bru, t, factor (5) = deviation_type (m (m_ noise) ), and the coefficients σi to ωe are determined in such a way as to obtain a maximum correlation between the subjective data from a subjective test database and the objective scores calculated by said method for the test signals, noises and corresponding processes used. during subjective tests. The coefficients of this linear combination have the advantage of being able to be recalculated if new subjective test data substantially modify the previously established correlation. This makes it possible to improve an objective model fed by the method according to the invention, of calculating the annoyance due to noise in an audio signal processed by a noise reduction function, by a simple reconfiguration of the parameters of the method.

The invention also relates to a method for calculating an objective note of the annoyance due to noise in an audio signal, said method comprising a preliminary step of obtaining a predefined test audio signal containing a useful signal devoid of noise, and a noisy signal obtained by adding a predefined noise signal to said test signal, said method being characterized by including a loudness measurement and frame tone coefficient measurement of said noisy signal.

This method has the same advantages as the previous method, but applies to any noisy audio signal.

According to a preferred characteristic, this method according to the invention comprises the steps of:

- Calculation of mean loudness densities ^~ s _xh ( _m ) to the noisy signal frames, mean loudness densities S χb {m_ speech) of useful signal frames "m_parole" of the noisy signal, of average loudness densities

S.sub.i, (m_noise) with noise frames "m_noise" of the noisy signal, and tone coefficients a, u (m_noise) with noise frames "m_noise" of the noisy signal,

- Calculation of an objective note of noise annoyance in the noisy signal, from said average loudness densities and said calculated tone coefficients, and predefined weighting coefficients.

According to a preferred characteristic, the step of calculating mean loudness densities and tone coefficients is followed by a calculation step average s M, S x _{b ^} speech, MS _^ brmt and axi, bmn desd ^SIU densities average loudness of said tone coefficients over all the relevant frames of the corresponding signals, and in that said objective score of the noise interference is calculated according to the following equation:

4

NOB =] T ωf actor (i) + ωs,

where _r . _{I Λ \} S Xb noise factor (1) = - =

S Xb Sxb noise factor (2) =

S Xh _ word factor (3) = cκ »_w, factor (4) = Distance Jy pe (aχb (m_ noise)), the operator" Standard deviation (v (m)) "designating the standard deviation of the variable v over the set of frames of index m, and the coefficients ωi to ωs are determined so as to obtain a maximum correlation between the subjective data from a database of subjective tests and the objective scores calculated by said method for the test signals and the corresponding noisy signals used in said subjective tests.

As for the previous method, the coefficients of this linear combination have the advantage of being able to be recalculated if new subjective test data substantially modify the previously established correlation. This makes it possible to improve an objective model fed by the method according to the invention, of calculating the annoyance due to the noise in an audio signal, by a simple reconfiguration of the parameters of the method.

According to a preferred feature of these two methods according to the invention, the step of calculating loudness densities and tone coefficients is preceded by a voice activity detection step sot the test signal, so as to determine if a current frame of the noisy signal, and the signa! treated in the case of the first method, is a "m_noise" frame containing only noise, or a "m_parole" frame containing speech, called a useful signal frame.

This voice activity detection step makes it possible to very simply separate the different types of frames of the noisy signal, and of the signal processed in the case of the first method, by the use of the test signal.

According to a preferred characteristic of these two methods according to the invention, the step of calculating the objective score is followed by a step of calculating an objective score on the MOS scale of the annoyance due to the noise, calculated according to the following equation:

4 NOB _ MOS = Σλ _t (NOB) '- ¹ ,

wherein the coefficients λ i to λ ₄ are determined so that said new obtained objective score characterizes the noise annoyance on the MOS scale.

The fact of using a polynomial function of order 3 makes it possible to obtain an objective score on the MOS scale very close to the subjective MOS score that would be given by a group of listeners in the context of a subjective test conforming to the " ITU-T Recommendation P.835 ".

According to a preferred characteristic of these two methods according to the invention, the step of calculating loudness densities and tone coefficients, calculating the average loudness density Su (m) of a frame of any index m, a given audio signal u, comprises the following steps:

windowing, for example of the Hanning type, of the frame of index m and obtaining a windowed frame u_w [m],

applying a fast Fourier transform to the windowed frame u_w [m] and obtaining a corresponding frame U (m, f) in the frequency domain,

calculation of the spectral power density γ _{ (m, f) of the frame U (m, f), application to the power spectral density γ _υ (m, f) of a conversion of the frequency axis to the Barks scale and obtaining a spectral density of power B, _j (m, b) on the Barks scale,

- Convolution of the spectral power density on the Barks scale, B _Li (m, b), with the spreading function commonly used in psychoacoustics and obtaining a spectral density spread on the Barks, Eu scale ( m, b),

calibration of the spectral density spread on the Barks scale, E _1J (m, b), by the respective power scaling and loudness scaling factors commonly used in psychoacoustics, conversion of the magnitude thus obtained onto the the scale of the phones then conversion on the scale of the sones of the quantity previously converted into phones, and consequently obtaining a number B of loudness density values, S ₁ , (m, b), of the frame of index m for the critical band b, where B is the number of critical bands considered in the Barks scale and the index b ranging from 1 to B,

calculating the average loudness density Su (m) of the frame of index m from the said B loudness density values S _u (m, b), according to the following equation: Su (m) = - Σ S _u (m, b) t> b = l

According to a preferred characteristic of these two methods according to the invention, in the step of calculating loudness densities and tone coefficients, the calculation of the tone coefficient α (m) of a frame of any index m of a given audio signal u comprises the following steps: windowing, for example of the Hanning type, of the frame of index m and obtaining a windowed frame u_w [m], applying a fast Fourier transform to the windowed frame u w w [m] and obtaining a corresponding frame U (m, f) in the frequency domain, calculating the power spectral density γu (m, f) of the frame U (m, f), calculation of the tone coefficient α (m) according to the following equation:

where ^* symbolizes the multiplication operator in the real number space, f represents the frequency index of the power spectral density, and N denotes the size of the fast Fourier transform.

The invention also relates to a test equipment for evaluating an objective note of the annoyance due to noise in an audio signal, characterized in that it comprises means adapted to implement one or the other of the methods according to the invention. According to a preferred characteristic, the test equipment includes computer means and a computer program, said program comprising instructions adapted to implement one or the other of said methods, when it is executed by said computer means. .

The invention also relates to a computer program on an information carrier _" comprising instructions adapted to the implementation of one or the other of the methods according to the invention, when the program is loaded and executed in a computer system.

The advantages of this test equipment or computer program are identical to those mentioned above in relation to the methods of the invention, Other features and advantages will become apparent on reading preferred embodiments described with reference to the figures in which: FIG. 1 represents a test environment intended to calculate an objective score of the annoyance due to the noise in an audio signal processed by a noise reduction function, according to a first embodiment of the invention,

FIG. 2 is a flowchart illustrating a method for calculating an objective note of the annoyance due to noise in an audio signal processed by a noise reduction function according to a first embodiment of the method according to the invention,

FIG. 3 is a flowchart illustrating a method for calculating an objective note of the annoyance due to noise in an audio signal according to a second embodiment of the method according to the invention,

FIG. 4 is a flowchart illustrating the method of calculating the mean loudness density and the tone coefficient of an audio signal frame according to the invention.

Two embodiments of the method according to the invention are described hereinafter, the first being applied to an audio signal processed by a noise reduction function, and the second being applied to any noisy audio signal. The principle of the method according to the invention is the same in these two embodiments, in particular the calculation method is exactly the same, but in the second embodiment ie signa! audio processed by a noise reduction function is taken equal to the signa! noisy. The second embodiment can indeed be considered as a special case of the first embodiment, with an inhibited noise reduction function. According to the first embodiment of the method of the invention, the annoyance due to the presence of noise in an audio signal processed by a function of noise reduction is objectively evaluated in a test environment shown in FIG. 1. Such a test environment comprises a source of SSA audio signals delivering a test audio signal x (n) containing only the wanted signal, and that is to say, without noise, for example a speech signal, and a noise source SB delivering a predefined noise signal.

For testing purposes, this predefined noise signal is added to the selected test signal x (n), as represented by the AD addition operator. The audio signal resulting from this addition of noise to the test signal x (n) is denoted xb (n) and is designated by the expression "noisy signal".

The noisy signal xb (n) then constitutes the input signal of a module

MRB noise reduction implementing a noise reduction function outputting an audio signal y (n) designated by the expression "processed signal". The processed signal y (n) is therefore an audio signal containing useful signal and residual noise.

The processed signal y (n) is then delivered to a test equipment EQT implementing a method of objective evaluation of the annoyance due to the noise in the processed signal, according to the invention. Typically the method according to the invention is implemented in the EQT test equipment in the form of a computer program. In addition to or in replacement of software means, the EQT test equipment optionally comprises electronic hardware to implement the method according to the invention. In addition to the signal y (n), the test equipment EQT receives as input the test signal x (n) and the noisy signal xb (n). The EQT test equipment outputs an evaluation result

RES ₅ which is an objective note NOBJVIOS its annoyance due to the presence of noise in the processed signal y (n). The method of calculation of this objective note NQBJvIOS will be described below.

The aforementioned audio signals x (n), xb (n) and y (n) are signals sampled in a digital format, n designating a sample any. These signals are for example supposed to be sampled at the sampling frequency of 8 kHz (kilo Hertz).

In the embodiment described and shown here, the test signal x (n) is a speech signal devoid of noise. The noisy signal xb (n) then represents the initial speech signal x (n) degraded by a noisy environment (background or ambient noise), and the signal y (n) represents the signal xb (n) after noise reduction.

According to an exemplary implementation of the invention, the signal x (n) is generated in an anechoic chamber. However, the signal x (n) can also be generated in a "quiet" room having an "average" reverberation time of less than 0.5 seconds.

The noisy signal xb (n) is obtained by adding a predetermined contribution of noise to the signal x (n). The signal y (n) is obtained either at the output of a noise reduction algorithm implanted on a personal computer, or at the output of a noise reduction network equipment and in the latter case, the signal y (n) is taken at the level of a PCM encoder (pulse modulation and coding).

With reference to FIG. 2, the method for calculating the objective note NOB_MOS of the annoyance due to the noise in the processed signal y (n) according to the invention is represented in the form of an algorithm comprising steps a1 to a7.

In a first step ai, the signals x (n), xb (n) and y (n) are respectively divided into successive time windows called frames. Each signal frame, noted m, contains a predetermined number of samples of the signal, step a thus consists of a change of rate of each of these signals. The signals x (n), xb (n) and y {n) passed in frame rate respectively produce the signals x [m], xb [m], and y [m].

In a second step a2, voice activity detection (DAV) is performed on the signal x [m] so as to determine whether each respective current frame of index m has signals xb [m] and y [m]. is a frame containing only noise, denoted "m noise", or a frame containing ia speech, that is to say the useful signal, and noted "m_parole". This determination is made by comparing the signals xb [m] and y [m] with the test signal x [m] devoid of noise. Each silence frame of x [m] corresponds in fact to a noise frame for the signals xb [m] and y [m], while each speech frame of x [m] corresponds to a speech frame for the signals xb [m] and y [m].

As represented in FIG. 2, at the output of step a2, three types of frames are selected from the signals x [m], xb [m] and y [m]:

- the speech frames of the noisy signal xb [m], denoted xb [m_parole], - the speech frames of the processed signal y [m], denoted y [m_parole],

the noise frames of the processed signal y [m], denoted y [m_noise].

In a third step a3, loudness measurements are made on at least sets of y [m_noise], y [m_parole], xb [m_parole] frames from the previous step a2, and at least one set of frames of the signal y [m] at the output of step ai. For example, if 8 seconds of sampled test signal at 8 kHz is used, it will be possible to work on 250 fields y [m] of 256 samples of signal y (n). In addition, the tone coefficients of at least one set of y [m_noise] frames are measured.

More specifically, in this step, one calculates the loudness densities average ^~ Sχ _b (word m_), ^~ S ^~ γ {m_ speech), ^~ Sγ {m), and £ y _(m _bruit) of respectively each of the frames xb [m_parole], y [m_parole], y [m] and y [rn_noise] sets of frames considered. Similarly, the tone coefficients a _> - (m noise) of each of the frames y [m_noise] of the considered set of frames y [m] are calculated. The calculation of an average loudness density Su (m) and a tone coefficient α (m) of a frame of any index m of a given audio signal u, will be detailed later in connection with FIG. 4.

In a fourth step a4. the respective averages are calculated

^~ S "• _ rarok S nato S _^ and 5% fu t average response densities Sv, (w _ speech) • ^~ S <(m _ speech) ^~ Sï (m) and ^~ S, (m _brtut) previously calculated on the respective sets considered frames xb [m_parole], y [m_parole], y [m] and y [m_noise]. The mean ccr_noise of the tone coefficients a, γ {m noise) previously calculated on the considered set of frames y [m_noise] is also calculated.

In a fifth step a5, five factor factors (i) are calculated, i being an integer varying from one to five, characteristic of the annoyance due to the noise in the signal y (n), according to the following formulas; _r,, . . Sr noise factor (1) = - = -,

Sr e _t . _t n _\ Si noise factor (2) = -

SY_ word factor (3) = Type_offset speech), the operator

"Deviation_type (v (m))" designating the standard deviation of the variable v over the set of frames m, factor (4) = ar_bmt, factor r (5) = Span_type (m (m_ noise)). In a sixth step a6, the calculation of an intermediate objective score

NOB is obtained by linear combination of the five factors calculated in step a5, according to the following equation:

5

NOB - Σ o) fi actor (ï) + <y ",

M where the coefficients ωi to ω _% are predefined weighting coefficients. These coefficients were determined in order to obtain a maximum correlation between the subjective data from a subjective test database, and the objective scores NOB calculated by this linear combination using the test signals, noisy and processed x [m ], xb [m] and y [m] used in these same subjective tests. The subjective test database is, for example, a database of scores obtained with groups of listeners in accordance with the "Recommendation UiT-T P.835 ™, in which these notes are called background noise notes. It should be noted that the obtaining of the weighting coefficients by the use of a database of subjective tests is not essential for each step of calculating an objective score NOB. Indeed, these coefficients must be obtained prior to the first use of the process, and may be the same for all uses of the process. These coefficients are nevertheless likely to evolve when new subjective data come to feed the database of subjective tests used.

Finally, in a last step a7, an objective note NOB_MOS of the annoyance due to the noise in the processed signal y (n) on the MOS scale is calculated using for example a polynomial function of order 3, according to the following equation:

NOB _ MOS = Σ X ₁ (NOB) ^w ,

where the coefficients λi to λ ₄ are determined so that the objective score obtained NOB_MOS characterizes the annoyance due to the noise on the MOS scale, that is to say on a scale of 1 to 5.

According to a second embodiment of the method of the invention, the annoyance due to the presence of noise in any noisy audio signal is evaluated objectively. The same test environment is used as in Figure 1, but by removing the MRB noise reduction module. The audio signal source SSA delivers a test audio signal x (n) containing only the wanted signal, to which is added a predefined noise signal generated by the noise source SB, to obtain at the output of the addition operator AD a noisy signal xb (n). The test signal x (n) and the noisy signal xb (n) are then directly sent to the input of the test equipment EQT implementing a method of objective evaluation of the annoyance due to the noise in the noisy signal. According to the invention, as in the first embodiment, the signals x (n) and xb (n) are assumed to be sampled at the sampling frequency 8 kHz. The test equipment EQT outputs an evaluation result RES, which is an objective note NOB_MOS of the annoyance due to the presence of noise in the noisy signal xb (n).

With reference to FIG. 3, the method for calculating the objective noise-noise rating NOB-MOS in the noisy signal xb (n) according to the invention is represented in the form of an algorithm comprising steps b1 to b7.

These steps are similar to the steps ai to a7 previously described in the first embodiment, and will therefore be a little less detailed. It should be noted that if we apply the calculation steps a3 to a7 with the signal y (n) equal to the signal xb (n) in the case of the first embodiment, we reach the second embodiment.

In a first step b1, the signals x (n) and xb (n) are split into frames x [m] and xb [m] of time index m.

In a second step b2, a voice activity detection is performed on the signal x [m] so as to determine whether each current frame of index m of the noisy signal xb [m] is a frame containing only noise, denoted " m_noise ", or a frame also containing speech, denoted" m_parole ". Two types of frames are thus selected from the signals x [m] and xb [m] at the output of step b2: the speech frames of the noisy signal xb [m], denoted xb [m_parole],

and the noise frames of the noisy signal xb [m], denoted xb [m_noise]. In a third step b3, loudness measurements are made on at least sets of frames xb [m_noise] and xb [m_parole] from the previous step b2, and at least one set of frames of the signal xb [m] in exit from step b1. More tone coefficients ^* at least one set of frames kB [m_noise] are measured.

More precisely, at this step, the average output densities Si /. (»I), ^~ Sxb (m _ speech) and ^~ Sχb (m _hruit) of respectively each of the frames xb [m], xb [m_parole] are calculated. and xb [m_noise] sets of frames considered. Similarly, the tone coefficients a-Mm noise) of each frames xb [m_noise] of the considered set of frames xb [m_noise] are calculated.

In a fourth step b4, the respective averages are calculated

^~ Sy,, ÏSλb _ speech, and IM average loudness densities ^~ s. _Α ( _m ), ^~ Sxb (m speech) and ^~ s.xb (m noise) previously calculated on the respective sets considered frames xb [m], xb [m_parole] and xb [m_noise]. The average ton coefficients aw (m_noise) previously calculated on the considered set of frames xb [m_noise] is also calculated. In a fifth step b5, four factors factor (i) are calculated, i being an integer varying from one to four, characteristic of the annoyance due to the noise in the noisy signal xb (n), according to the following formulas: i i. _{/ A \} S Xb noise factor (1) = - ==,

S Xb x i. _/ <-Is S Xb noise factor (2) = = -

Sxb _ Factor word r (3) = cCxt __ bmu, factor (4) = the operator "Ecart_type (v (m))" designating the standard deviation of the variable v on the set of frames m.

In a sixth step b6, the calculation of an intermediate objective score NOB is obtained by linear combination of the four factors calculated in step b5, according to the following equation:

NOB =] T ohfactor (i) -r ωs,

where the coefficients ωi to ωs are predefined weighting coefficients. These coefficients were determined in order to obtain a maximum correlation between the subjective data from a subjective test database, and the objective scores NOB calculated by this linear combination using the test signals and the noisy signals x [m ] and xb [m] used in these same subjective tests. Just as for step a6, obtaining the weighting coefficients by the use of a database of subjective tests is not indispensable at each step of calculating an objective score NOB.

Finally, in a last step b7, an objective note NOBJVlOS of the annoyance due to the noise in the noisy signal xb (n) on the MOS scale is calculated using for example a polynomial function of order 3, according to the following equation:

NOB _ MOS =] T λ _i (NOB) '-', i- \ where the coefficients λi to λ ₄ are determined in such a way that the objective score obtained NOB_MOS characterizes the annoyance due to the noise on the MOS scale, c on a scale of 1 to 5.

The calculation of the mean loudness density and the tone coefficient of a frame of an audio signal, used in steps a3 and b3, is now described in relation to FIG. 4, according to a preferred embodiment of the invention. .

The calculation according to the invention of the mean loudness density Su (m) of a frame of any index m of a given audio signal u [m], comprises the steps d to c7 shown in FIG. -after. The calculation according to the invention of the tone coefficient α (m) of a frame of any index m of a given audio signal u [m], comprises the steps d, c2, c3 and c8 shown in FIG. described below.

In what follows, we consider a frame of any index m of a signal [u], knowing that all or part of the frames of the signal considered undergo the same treatment. The signal u [mj represents any of the signals x [m], xb [m], or y [m] defined above.

Ia A first step _"is applied to ia frame of index m of the signal u [mj windowing, e.g. type windowing Hanning, Hamming or equivalent. We then obtain a windowed frame u_w [m]. In the next step c2, a fast Fourier transform (FFT) is applied to the windowed frame u_w [m] and a corresponding frame U (m, f) in the frequency domain is accordingly obtained.

At the next step c3, the power spectral density γy (m, f) of the frame U (m, f) is calculated. Such a calculation is known to those skilled in the art and will not, therefore, be detailed here.

At the end of step c3, for the signal y [m_noise] of step a3 or the signal xb [m_noise] of step b3, for example, step c8 is used to calculate the coefficient of tone, then at step c4 for the calculation of the average loudness density Su (m), since for these two signals the two calculations are necessary. For the other signals of steps a3 and b3, step c4 is used to calculate the average loudness density Su (m). It should be noted that the calculation of the tone coefficient is independent of the calculation of the mean loudness density Su (m), the two calculations can therefore be carried out in parallel or one after the other.

In step c4, a frequency conversion of the frequency axis at the Barks scale is applied to the power spectral density γu (m, f) obtained in the previous step, and a spectral density is consequently obtained. of power, B, _j (m, b), on the Barks scale, also called Bark's spectrum. For a sampling frequency of 8kHz, 18 critical bands must be considered. This type of conversion is known to those skilled in the art, the principle of this Hertz / Bark conversion consists in adding all the frequential contributions present in the critical band of the Barks scale. Applies to the spectral power density on the Barks scale, By (m, b), a convolution with the spreading function commonly used in psychoacoustics, and a spectral density spread on the scale of Barks _" noted E _L , (m, b). spreading function has been mathematically formulated and one possible expression is:

101oglO (£ (è)) = 15.81 + 7.5 * (b + 0.474) - 17.5 * y (1 + Φ + 0.474) ² ), where E (b) is the spreading function applied to the critical band b considered in the Barks scale and * symbolizes the multiplication operator in the real number space. This step makes it possible to take into account the interaction of the adjacent critical bands.

At the next step c6, the spread spectral density is converted

E _1J (m, b) obtained previously in loudness densities expressed in sones. For this, a calibration of the spectral density spread on the Barks scale, Ey (m, b), is performed by the respective power scaling and loudness scaling factors commonly used in psychoacoustics. The document "ITU-T Recommendation P.862", sections 10.2.1.3 and 10.2.1.4, gives an example of such a calibration by the aforementioned factors. The size obtained is then converted on the scale of the phones. The conversion on the scale of the phones is carried out based on the isosonic curves (Fletcher curves) in accordance with the standard NF ISO 226 "Normal isosonic lines". We then perform a conversion on the scale of sones of the size previously converted to phones. The conversion to sones is effected in accordance with Zwicker's law according to which: f N (phonc) Û0 ^x ;

N (sone) = 2 ^ ¹⁰ '

For more information on the conversion of phone / sone, see the document "PSYCHOACOUSTICA, The ear receiving information", by E. Zwicker and R. Feldtkeller, Masson edition, 1981.

At the end of step c6, there is a number B of loudness density values, S ₁ ; (m, b), of the frame of index m for the critical band b, B being the number of critical bands considered in the Barks scale and the index b varying from 1 to B. Finally, in step c7, the average loudness density Su (m) of the frame of index m is calculated from said B loudness density values, according to the following equation:

Sϋ (m) = ΣSu (m, b)

B _{b = i} In other words, the average loudness density Su (m) according to the invention of a frame of index m, is therefore the average of the B loudness density values S _LI (m, b), of the frame of index m for a critical band b considered.

These last two steps c6 and c7 correspond to a conversion of the Barks domain to the Sones domain, making it possible to calculate a mean subjective intensity, that is to say as perceived by the human ear.

In addition, in step c8, the tone coefficient α (m) of the frame of index m is calculated according to the following equation:

where ^* symbolizes the multiplication operator in the real number space, f represents the frequency index of the power spectral density, and N denotes the size of the fast Fourier transform. This calculation is carried out according to the principle defined by JD Johnston in his article "Transforming coding of audio signals using perceptual noise criteria" of the journal "IEEE Journa! On selected areas in communications _" vol.6, n ° 2, February 1988 ".

The tone coefficient α of a basic signal is a measure to show whether certain pure frequencies emerge from this signal. It is equivalent to a tonal density. Indeed, the closer the tone coefficient α is to 0, the more the signal is assimilated to noise, the more the tone coefficient α is close to 1, the more the signal is component tonal majority. A tone coefficient α close to 1 attests to the presence of useful signal, or speech signal.

Claims

A method of calculating an objective score (NOB) of noise annoyance in an audio signal processed by a noise reduction function, said method comprising a prior step of obtaining a predefined audio test signal ( x [m]) containing a useful signal devoid of noise, a noisy signal (xb [m]), obtained by adding a predefined noise signal to said test signal (x [m]), and a processed signal (y [m]), obtained by applying the noise reduction function to said noisy signal (xb [m]), said method being characterized in that it includes a frame loudness measurement step (a3, a4) said noisy signal (xb [m]) and said processed signal (y [m]), and measurements of frame pitch coefficients of said processed signal (y [m]).

2. Method according to claim 1, characterized in that it comprises the steps of:

Calculation (a3) of average loudness densities & (m) of frames of the processed signal (y [m]), respective mean loudness densities ^~ Sxb (m-speech) & 5 V (m-per-life) of frames of useful signal "m_parole" respectively of the noisy signal (xb [m]) and of the processed signal (y [m]), of mean loudness densities Sγ (m_noise) with "m_noise" noise frames of the processed signal (y [m] |), and tone coefficients aγ (m_noise) with noise frames "m_bruiî" of the processed signal (y [m]}, - Calculation (a5, a6) of an objective note (NOB) of noise annoyance in the processed signal (y [mj), from said average loudness densities and said calculated tone coefficients, and predefined weighting coefficients.

3. Method according to claim 2, characterized in that the step of calculating (a3) mean loudness densities and tone coefficients is followed by a calculation step (a4) means 5Y, 5, _ _ speech , Sr __ speech, Sr _bruu - and CC _Y noise of said average loudness densities and said tone coefficients over all the relevant frames of the corresponding signals, and in that the objective note (NOB) of the annoyance due to the noise is calculated according to the following equation:

INO? = ^ ωfactor (i) + ωo,

1 = 1

WHERE _,. . . factor (1) =,

_Γ , ,, -... Sr noise factor (2) = = -

Sr_ word factor (3) = Standard deviation [Sv> (m _ speech) - Sγ (m_ speech) J, the operator "Standard deviation (v (m))" denoting the standard deviation of the variable v over the set frames of index m, factor (4) = ar_bruu, factor (5) = Standard deviation (ar (m_noise)), and the coefficients ωi to K> Q are determined so as to obtain a maximum correlation between the subjective data from a database of subjective tests and the objective scores (NOB) calculated by said method for corresponding test, noisy and processed signals (x [m], xb [m], y [m]) used during the experiments subjective tests.

4. A method of calculating an objective note (NOB) of the annoyance due to noise in an audio signal, said method comprising a preliminary step of obtaining a predefined test signal (x [m]) containing a signal ! useful noise-free, and a noisy signal (xb [m]), obtained by adding a predefined noise signal to said signa! test (x [m]), said method being characterized by including a step (b3, b4) of loudness measurements and frame tone coefficient measurements of said noisy signal (xb [m]).

5. Method according to claim 4, characterized in that it comprises the steps of:

- Calculation (b3) of mean loudness densities. («) Of noisy signal frames (xb [m]), mean loudness densities Sχb (m_ speech) of useful signal frames" m_parole "of noisy signal (xb [m]), mean loudness densities s, vj (m _bmit) to noise "m_noise" noise signal frames (xb [m]), and dxb {m_ noise tone noise noise frames)

"m_noise" of the noisy signal (xb [m]),

Calculation (b5, bδ) of an objective score (NOB) of the annoyance due to noise in the noisy signal (xb [m]), from said average loudness densities and said calculated tone coefficients, and predefined weighting.

6. Method according to claim 5, characterized in that the step of calculating (b3) mean loudness densities and tone coefficients is followed by a calculation step (b4) averages s ^, s, " α α / e, »">"" γ and _{M M} b b _r one of said average loudness densities and said tone coefficients over all of the relevant frames of the corresponding signals, and in that said objective score ( NOB) of the annoyance due to the noise is calculated according to the following equation:, ¥ 05 = Y ωf, actor (i) • - COS

OR

,. . . S brown Xb factor (1) == - ==

sxb factor (3) = av, _bruu, factor (4) = Ecaύjype (aχb (m_noise)), the operator "Ecart_type (v (m))" designating the standard deviation of the variable v over all frames of index m, and the coefficients ωi to ω ₅ are determined so as to obtain a maximum correlation between the subjective data from a subjective test database and the objective scores (NOB) calculated by said method for the signals of tests and the corresponding noisy signals (x [m], xb [m]) used in said subjective tests.

7. Method according to any one of claims 1 to 6, characterized in that said step of calculating (a3, b3, a4, b4) loudness densities and tone coefficients is preceded by a step (a2, b2 ) for detecting voice activity on the test signal, so as to determine whether a current frame of index m of the noisy signal (xb [m]), and of the processed signal (y [m]) in the case of the claims 1 to 3, is a "m_noise" frame containing only noise, or an "mj> arole" frame containing speech, called a useful signal frame.

8. Method according to any one of claims 1 to 7, characterized in that the step of calculating (a6, b6) of the objective score (NOB) is followed by a calculation step (a7, b7) of an objective score on the MOS scale (NOB_MOS) of noise annoyance _" calculated according to the following equation:

NOB _ MOS = K (NOB) '^{' 1} ,

in which the coefficients λi to » ₄ are determined in such a way that said new objective score obtained (NOBJvIOS) characterizes the discomfort due to noise on MOS fechase.

9. Method according to any one of claims 1 to 8, characterized in that, in the calculation step (a3, b3, a4, b4) of loudness densities and tone coefficients, the calculation of the density of mean sound Su (m) of a frame of any index m of a given audio signal u, comprises the following steps:

- windowing (d), for example of Hanning type, the frame of index m and obtaining a windowed frame u_w [m],

- applying (c2) a fast Fourier transform to the windowed frame u_w [m] and obtaining a corresponding frame U (m, f) in the frequency domain,

- calculation (c3) of the power spectral density yu On ,! ^" ) of the frame

U (m, f),

- application (c4) to the power spectral density γ, _j (m, f) of a conversion of the frequency axis to the Barks scale and obtaining a spectral power density By (In, b) on the Barks scale,

- convolution (c5) of the spectral power density on the Barks scale, B _L (m, b), with the spreading function commonly used in psychoacoustics and obtaining a spread spectral density on the Barks scale , Eu (m, b), - calibration (c6) of the spectral density spread on the Barks scale,

Eu (In ₅ I)), by the respective factors of power scaling and loudness scaling commonly used in psychoacoustics, conversion of the magnitude thus obtained on the scale of phones then conversion on the scale of the soπes of the magnitude previously converted to phones, and correspondingly obtaining a number B of loudness density values, S _L (mb). the frame of index m for the critical band b, where B is the number of critical bands considered in the barksheet and the index b varies from 1 to B, calculating (c7) the average loudness density SL (m) of the frame of index m from said B loudness density values S _L! (m, b), according to the following equation:

Su (m) = i ΣS _u (m, b)

^B b = l

10. Method according to any one of claims 1 to 9, characterized in that, in the step of calculating (a3, b3, a4, b4) of loudness densities and tone coefficients, the calculation of the tone coefficient α (m) of a frame of any index m of a given audio signal u, comprises the following steps:

windowing (d), for example of Haπning type, of the frame of index m and obtaining a windowed frame u_w [m],

calculation (c3) of the power spectral density γ ^ m ,! ^" ) of the frame

U (m, f),

calculation (c8) of the tone coefficient α (m) according to the following equation:

where * symbolizes the multiplication operator in the real number space, f represents the frequency index of the power spectral density, and N denotes the size of the fast Fourier transform.

11, Test equipment for evaluating an objective note of noise disturbance in a signa! audio, characterized in that it comprises means adapted to implement a method according to any one of claims 1 to 10.

12. Test equipment according to claim 11, characterized in that it includes computer means and a computer program, said program comprising instructions adapted to implement said method, when it is executed by said computer means.

13. Computer program on an information carrier, characterized in that it comprises instructions adapted to the implementation of a method according to any one of claims 1 to 10, when the program is loaded and executed in a computer system.