Abstract:
The present invention relates to a method of evaluating intelligibility of a degraded speech signal received from an audio transmission system conveying a reference speech signal. The method comprises sampling said reference and degraded signals into reference and degraded signal frames, and forming frame pairs by associating reference and degraded signal frames with each other. For each frame pair a difference function representing disturbance is provided, which is then compensated for specific disturbance types for providing a disturbance density function. Based on the density function of a plurality of frame pairs, an overall quality parameter is determined. The method provides for weighing disturbances in silent periods dependent on the loudness of the reference signal.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. National Stage application under 35 U.S.C. §371 of International Application PCT/NL2012/050808 (published as WO 2013/073944 A1), filed Nov. 15, 2012, which claims priority to Application EP 11189598.3, filed Nov. 17, 2011. Benefit of the filing date of each of these prior applications is hereby claimed. Each of these prior applications is hereby incorporated by reference in its entirety. 
     FIELD OF THE INVENTION 
     The present invention relates to a method of evaluating intelligibility of a degraded speech signal received from an audio transmission system, by conveying through said audio transmission system a reference speech signal such as to provide said degraded speech signal, wherein the method comprises: sampling said reference speech signal into a plurality of reference signal frames and determining for each frame a reference signal representation; sampling said degraded speech signal into a plurality of degraded signal frames and determining for each frame a degraded signal representation; forming frame pairs by associating each reference signal frame with a corresponding degraded signal frame, and providing for each frame pair a difference function representing a difference between said degraded signal frame and said associated reference signal frame. 
     The present invention further relates to an apparatus for performing a method as described above, and to a computer program product. 
     BACKGROUND 
     During the past decades objective speech quality measurement methods have been developed and deployed using a perceptual measurement approach. In this approach a perception based algorithm simulates the behaviour of a subject that rates the quality of an audio fragment in a listening test. For speech quality one mostly uses the so-called absolute category rating listening test, where subjects judge the quality of a degraded speech fragment without having access to the clean reference speech fragment. Listening tests carried out within the International Telecommunication Union (ITU) mostly use an absolute category rating (ACR) 5 point opinion scale, which is consequently also used in the objective speech quality measurement methods that were standardized by the ITU, Perceptual Speech Quality Measure (PSQM (ITU-T Rec. P.861, 1996)), and its follow up Perceptual Evaluation of Speech Quality (PESQ (ITU-T Rec. P.862, 2000)). The focus of these measurement standards is on narrowband speech quality (audio bandwidth 100-3500 Hz), although a wideband extension (50-7000 Hz) was devised in 2005. PESQ provides for very good correlations with subjective listening tests on narrowband speech data and acceptable correlations for wideband data. 
     As new wideband voice services are being rolled out by the telecommunication industry the need emerged for an advanced measurement standard of verified performance, and capable of higher audio bandwidths. Therefore ITU-T (ITU-Telecom sector) Study Group 12 initiated the standardization of a new speech quality assessment algorithm as a technology update of PESQ. The new, third generation, measurement standard, POLQA (Perceptual Objective Listening Quality Assessment), overcomes shortcomings of the PESQ P.862 standard such as incorrect assessment of the impact of linear frequency response distortions, time stretching/compression as found in Voice-over-IP, certain type of codec distortions and reverberations. 
     Although POLQA (P.863) provides a number of improvements over the former quality assessment algorithms PSQM (P.861) and PESQ (P.862), the present versions of POLQA, like PSQM and PESQ, fails to address an elementary subjective perceptive quality condition, namely intelligibility. Despite also being dependent on a number of audio quality parameters, intelligibility is more closely related to the quality of information transfer than to the quality of sound. In terms of the quality assessment algorithms, the nature of intelligibility as opposed to sound quality causes the algorithms to yield an evaluation score that mismatches the score that would have been assigned if the speech signal had been evaluated by a person or an audience. Keeping in focus the objective of information sharing, a human being will value an intelligible speech signal above a signal which is less intelligible but which is similar in terms of sound quality. The presently known algorithms will not be able to correctly address this to the extent required. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to seek a solution for the abovementioned disadvantage of the prior art, and to provide a quality assessment algorithm for assessment of (degraded) speech signals which is adapted to take intelligibility of the speech signal into account for the evaluation thereof. 
     The present invention achieves this and other objects in that there is provided a method of evaluating intelligibility of a degraded speech signal received from an audio transmission system, by conveying through said audio transmission system a reference speech signal such as to provide said degraded speech signal, wherein the method comprises: sampling said reference speech signal into a plurality of reference signal frames and determining for each frame a reference signal representation; sampling said degraded speech signal into a plurality of degraded signal frames and determining for each frame a degraded signal representation; forming frame pairs by associating each reference signal frame with a corresponding degraded signal frame, and providing for each frame pair a difference function representing a difference between said degraded signal frame and said associated reference signal frame; compensating said difference function for one or more disturbance types such as to provide for each frame pair a disturbance density function which is adapted to a human auditory perception model; deriving from said disturbance density functions of a plurality of frame pairs an overall quality parameter, said quality parameter being at least indicative of said intelligibility of said degraded speech signal; wherein, said method further comprises the steps of: determining a loudness value for each of said reference signal frames; and determining a weighting value dependent on said loudness value of said reference signal frame; wherein said step of compensating of said difference function comprises a step of weighing said difference function using said loudness dependent weighting value, for incorporating an impact of disturbance on said intelligibility of said degraded speech signal into said evaluation. 
     The present invention addresses intelligibility by recognising that noise and other disturbances are most destructive to the communication when information is particularly being carried over. In voice communications, this is during the time when the speech signal actually carries spoken words. Moreover, the invention correctly takes into account the modulating and variable nature of spoken language, and provides a manner of incorporating the destructive nature of disturbances and its dependency upon this modulating and variable nature of spoken language. By including a weighting value dependent on the loudness value of the reference signal, the method of the present invention allows for weighing the amount of disturbance dependent on whether or not information is actually being conveyed in the degraded speech signal. 
     According to an embodiment of the invention, for determining the loudness dependent weighting value, the method comprises a step of comparing said loudness value with a threshold, and making said weighting value dependent on whether said loudness value exceeds said threshold. As will be appreciated, comparing the loudness value with a threshold allows for using a different approach for the assessment of noise and disturbances during speech pauses and during spoken words. The impact of disturbance will be different during spoken words than during silent periods, and can be treated differently when use is made of a threshold. 
     According to a further embodiment, the weighting value is fixed to a maximum value when said loudness value for said reference signal frame exceeds said threshold. For example, above the threshold, the method of the present invention may simply apply a weighting value of 1.0 for fully including all disturbances during spoken words. 
     According to a further embodiment, the weighting value is a function which is dependent on the loudness value, for example when said loudness value for said reference signal frame is smaller than said threshold. Such a function may be a linear dependency, or another suitable dependency on the loudness value. According to a specific embodiment which in accordance with experiments provides good value the weighting value may be made equal to the loudness value when the loudness value for the reference signal frame is smaller than said threshold. 
     In accordance with a further embodiment, in addition to comparing the loudness value with a first threshold, for determining said loudness dependent weighting value, the method comprises a step of comparing the loudness value with a second threshold, wherein the weighting value is made smaller than a maximum value when the loudness value for the reference signal frame exceeds the second threshold. The second threshold in this embodiment is larger than the first threshold, and additionally allows for weighing disturbance differently dependent on whether the disturbance is encountered during pronunciation of a vowel or a consonant in the speech signal. It has been observed that disturbance during pronunciation of a consonant is experienced as more annoying to a listener than disturbance during a vowel. In accordance with a particular embodiment, when said loudness value for said reference signal frame exceeds the second threshold, the weighting value is made reversely dependent on an amount with which the loudness value exceeds the second threshold. 
     The loudness value may be determined as a single value for the whole frame, or it may be determined in a frequency dependent manner. In this latter case, the weighting value is made dependent on said frequency dependent loudness value. Loudness is a frequency dependent value, as it is a parameter that indicates how ‘loud’ a sound is perceived by a human ear, and the human ear can be regarded a frequency dependent audio sensor. This also reveals that disturbances may be detrimental to intelligibility dependent on the frequency of such disturbances. 
     The present invention may be applied to quality assessment algorithms such as POLQA or PESQ, or its predecessor PSQM. These algorithms are particularly developed to evaluate degraded speech signals. Within POLQA (perceptual objective listening quality assessment algorithm), the latest quality assessment algorithm which is presently under development, the reference speech signal and the degraded speech signal are both represented at least in terms of pitch and loudness. Determining the loudness value of a frame is therefore straightforward in POLQA, making application of the present invention in particular useful for this algorithm (P.863). 
     According to a second aspect, the invention is directed to a computer program product comprising a computer executable code for performing a method as described above when executed by a computer. 
     According to a third aspect, the invention is directed to an apparatus for performing a method as described above, for evaluating intelligibility of a degraded speech signal, comprising: a receiving unit for receiving said degraded speech signal from an audio transmission system conveying a reference speech signal, and for receiving said reference speech signal; a sampling unit for sampling of said reference speech signal into a plurality of reference signal frames, and for sampling of said degraded speech signal into a plurality of degraded signal frames; a processing unit for determining for each reference signal frame a reference signal representation, and for determining for each degraded signal frame a degraded signal representation; a comparing unit for forming frame pairs by associating each reference signal frame with a corresponding degraded signal frame, and for providing for each frame pair a difference function representing a difference between said degraded and said reference signal frame; a compensator unit for compensating said difference function for one or more disturbance types such as to provide for each frame pair a disturbance density function which is adapted to a human auditory perception model; and said processing unit further being arranged for deriving from said disturbance density functions of a plurality of frame pairs an overall quality parameter being at least indicative of said intelligibility of said degraded speech signal; wherein, said processing unit is further arranged for: determining a loudness value for each of said reference signal frames; and for determining a weighting value dependent on said loudness value of said reference signal frame; wherein said compensator unit is connected to said processing unit, and is further arranged for weighing of said difference function using said loudness dependent weighting value received from said processing unit. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is further explained by means of specific embodiments, with reference to the enclosed drawings, wherein: 
         FIG. 1  provides an overview of the first part of the POLQA perceptual model in an embodiment in accordance with the invention; 
         FIG. 2  provides an illustrative overview of the frequency alignment used in the POLQA perceptual model in an embodiment in accordance with the invention; 
         FIG. 3  provides an overview of the second part of the POLQA perceptual model, following on the first part illustrated in  FIG. 1 , in an embodiment in accordance with the invention; 
         FIG. 4  is an overview of the third part of the POLQA perceptual model in an embodiment in accordance with the invention; 
         FIG. 5  is a schematic overview of a masking approach used in the POLQA model in an embodiment in accordance with the invention; 
         FIG. 6  is a schematic illustration of the loudness dependent weighing of disturbance in accordance with the invention; 
         FIG. 7  is a schematic illustration of a further embodiment of the loudness dependent weighing of disturbance in accordance with the invention. 
     
    
    
     DETAILED DESCRIPTION 
     POLQA Perceptual Model 
     The basic approach of POLQA (ITU-T rec. P.863) is the same as used in PESQ (ITU-T rec. P.862), i.e. a reference input and degraded output speech signal are mapped onto an internal representation using a model of human perception. The difference between the two internal representations is used by a cognitive model to predict the perceived speech quality of the degraded signal. An important new idea implemented in POLQA is the idealisation approach which removes low levels of noise in the reference input signal and optimizes the timbre. Further major changes in the perceptual model include the modelling of the impact of play back level on the perceived quality and a major split in the processing of low and high levels of distortion. 
     An overview of the perceptual model used in POLQA is given in  FIG. 1 through 4 .  FIG. 1  provides the first part of the perceptual model used in the calculation of the internal representation of the reference input signal X(t) 3 and the degraded output signal Y(t) 5. Both are scaled  17 ,  46  and the internal representations  13 ,  14  in terms of pitch-loudness-time are calculated in a number of steps described below, after which a difference function  12  is calculated, indicated in  FIG. 1  with difference calculation operator  7 . Two different flavours of the perceptual difference function are calculated, one for the overall disturbance introduced by the system using operators  7  and  8  under test and one for the added parts of the disturbance using operators  9  and  10 . This models the asymmetry in impact between degradations caused by leaving out time-frequency components from the reference signal as compared to degradations caused by the introduction of new time-frequency components. In POLQA both flavours are calculated in two different approaches, one focussed on the normal range of degradations and one focussed on loud degradations resulting in four difference function calculations  7 ,  8 ,  9  and  10  indicated in  FIG. 1 . 
     For degraded output signals with frequency domain warping  49  an align algorithm  52  is used given in  FIG. 2 . The final processing for getting the MOS-LQO scores is given in  FIG. 3  and  FIG. 4 . 
     POLQA starts with the calculation of some basic constant settings after which the pitch power densities (power as function of time and frequency) of reference and degraded are derived from the time and frequency aligned time signals. From the pitch power densities the internal representations of reference and degraded are derived in a number of steps. Furthermore these densities are also used to derive  40  the first three POLQA quality indicators for frequency response distortions  41  (FREQ), additive noise  42  (NOISE) and room reverberations  43  (REVERB). These three quality indicators  41 ,  42  and  43  are calculated separately from the main disturbance indicator in order to allow a balanced impact analysis over a large range of different distortion types. These indicators can also be used for a more detailed analysis of the type of degradations that were found in the speech signal using a degradation decomposition approach. 
     As stated four different variants of the internal representations of reference and degraded are calculated in  7 ,  8 ,  9  and  10 ; two variants focussed on the disturbances for normal and big distortions, and two focussed on the added disturbances for normal and big distortions. These four different variants  7 ,  8 ,  9  and  10  are the inputs to the calculation of the final disturbance densities. 
     The internal representations of the reference  3  are referred to as ideal representations because low levels of noise in the reference are removed (step  33 ) and timbre distortions as found in the degraded signal that may have resulted from a non optimal timbre of the original reference recordings are partially compensated for (step  35 ). 
     The four different variants of the ideal and degraded internal representations calculated using operators  7 ,  8 ,  9  and  10  are used to calculate two final disturbance densities  142  and  143 , one representing the final disturbance  142  as a function of time and frequency focussed on the overall degradation and one representing the final disturbance  143  as a function of time and frequency but focussed on the processing of added degradation. 
       FIG. 4  gives an overview of the calculation of the MOS-LQO, the objective MOS score, from the two final disturbance densities  142  and  143  and the FREQ  41 , NOISE  42 , REVERB  43  indicators. 
     Pre-Computation of Constant Settings 
     FFT Window Size Depending on the Sample Frequency 
     POLQA operates on three different sample rates, 8, 16, and 48 kHz sampling for which the window size W is set to respectively 256, 512 and 2048 samples in order to match the time analysis window of the human auditory system. The overlap between successive frames is 50% using a Hann window. The power spectra—the sum of the squared real and squared imaginary parts of the complex FFT components—are stored in separate real valued arrays for both, the reference and the degraded signal. Phase information within a single frame is discarded in POLQA and all calculations are based on the power representations, only. 
     Start Stop Point Calculation 
     In subjective tests, noise will usually start before the beginning of the speech activity in the reference signal. However one can expect that leading steady state noise in a subjective test decreases the impact of steady state noise while in objective measurements that take into account leading noise it will increase the impact; therefore it is expected that omission of leading and trailing noises is the correct perceptual approach. Therefore, after having verified the expectation in the available training data, the start and stop points used in the POLQA processing are calculated from the beginning and end of the reference file. The sum of five successive absolute sample values (using the normal 16 bits PCM range—+32,000) must exceed 500 from the beginning and end of the original speech file in order for that position to be designated as the start or end. The interval between this start and end is defined as the active processing interval. Distortions outside this interval are ignored in the POLQA processing. 
     The Power and Loudness Scaling Factor SP and SL 
     For calibration of the FFT time to frequency transformation a sine wave with a frequency of 1000 Hz and an amplitude of 40 dB SPL is generated, using a reference signal X(t) calibration towards 73 dB SPL. This sine wave is transformed to the frequency domain using a windowed FFT in steps  18  and  49  with a length determined by the sampling frequency for X(t) and Y(t) respectively. After converting the frequency axis to the Bark scale in  21  and  54  the peak amplitude of the resulting pitch power density is then normalized to a power value of 10 4  by multiplication with a power scaling factor SP  20  and  55  for X(t) and Y(t) respectively. 
     The same 40 dB SPL reference tone is used to calibrate the psychoacoustic (Sone) loudness scale. After warping the intensity axis to a loudness scale using Zwicker&#39;s law the integral of the loudness density over the Bark frequency scale is normalized in  30  and  58  to 1 Sone using the loudness scaling factor SL  31  and  59  for X(t) and Y(t) respectively. 
     Scaling and Calculation of the Pitch Power Densities 
     The degraded signal Y(t) 5 is multiplied 46 by the calibration factor C  47 , that takes care of the mapping from dB overload in the digital domain to dB SPL in the acoustic domain, and then transformed  49  to the time-frequency domain with 50% overlapping FFT frames. The reference signal X(t) 3 is scaled  17  towards a predefined fixed optimal level of about 73 dB SPL equivalent before it&#39;s transformed  18  to the time-frequency domain. This calibration procedure is fundamentally different from the one used in PESQ where both the degraded and reference are scaled towards predefined fixed optimal level. PESQ pre-supposes that all play out is carried out at the same optimal playback level while in the POLQA subjective tests levels between 20 dB to +6 to relative to the optimal level are used. In the POLQA perceptual model one can thus not use a scaling towards a predefined fixed optimal level. 
     After the level scaling the reference and degraded signal are transformed  18 ,  49  to the time-frequency domain using the windowed FFT approach. For files where the frequency axis of the degraded signal is warped when compared to the reference signal a dewarping in the frequency domain is carried out on the FFT frames. In the first step of this dewarping both the reference and degraded FFT power spectra are preprocessed to reduce the influence of both very narrow frequency response distortions, as well as overall spectral shape differences on the following calculations. The preprocessing  77  consists in performing a sliding window average in  78  over both power spectra, taking the logarithm  79 , and performing a sliding window normalization in  80 . Next the pitches of the current reference and degraded frame are computed using a stochastic subharmonic pitch algorithm. The ratio  74  of the reference to degraded pitch ration is then used to determine (in step  84 ) a range of possible warping factors. If possible, this search range is extended by using the pitch ratios for the preceding and following frame pair. 
     The frequency align algorithm then iterates through the search range and warps  85  the degraded power spectrum with the warping factor of the current iteration, and processes  88  the warped power spectrum as described above. The correlation of the processed reference and processed warped degraded spectrum is then computed (in step  89 ) for bins below 1500 Hz. After complete iteration through the search range, the “best” (i.e. that resulted in the highest correlation) warping factor is retrieved in step  90 . The correlation of the processed reference and best warped degraded spectra is then compared against the correlation of the original processed reference and degraded spectra. The “best” warping factor is then kept  97  if the correlation increases by a set threshold. If necessary, the warping factor is limited in  98  by a maximum relative change to the warping factor determined for the previous frame pair. 
     After the dewarping that may be necessary for aligning the frequency axis of reference and degraded, the frequency scale in Hz is warped in steps  21  and  54  towards the pitch scale in Bark reflecting that at low frequencies, the human hearing system has a finer frequency resolution than at high frequencies. This is implemented by binning FFT bands and summing the corresponding powers of the FFT bands with a normalization of the summed parts. The warping function that maps the frequency scale in Hertz to the pitch scale in Bark approximates the values given in the literature for this purpose, and known to the skilled reader. The resulting reference and degraded signals are known as the pitch power densities PPX(f) n  (not indicated in  FIG. 1 ) and PPY(f n )  56  with f the frequency in Bark and the index n representing the frame index. 
     Computation of the Speech Active, Silent and Super Silent Frames (Step  25 ) 
     POLQA operates on three classes of frames, which are distinguished in step  25 : 
     speech active frames where the frame level of the reference signal is above a level that is about 20 dB below the average, 
     silent frames where the frame level of the reference signal is below a level that is about 20 dB below the average and 
     super silent frames where the frame level of the reference signal is below a level that is about 35 dB below the average level. 
     Calculation of the Frequency, Noise and Reverb Indicators 
     The global impact of frequency response distortions, noise and room reverberations is separately quantified in step  40 . For the impact of overall global frequency response distortions, an indicator  41  is calculated from the average spectra of reference and degraded signals. In order to make the estimate of the impact for frequency response distortions independent of additive noise, the average noise spectrum density of the degraded over the silent frames of the reference signal is subtracted from the pitch loudness density of the degraded signal. The resulting pitch loudness density of the degraded and the pitch loudness density of the reference are then averaged in each Bark band over all speech active frames for the reference and degraded file. The difference in pitch loudness density between these two densities is then integrated over the pitch to derive the indicator  41  for quantifying the impact of frequency response distortions (FREQ). 
     For the impact of additive noise, an indicator  42  is calculated from the average spectrum of the degraded signal over the silent frames of the reference signal. The difference between the average pitch loudness density of the degraded over the silent frames and a zero reference pitch loudness density determines a noise loudness density function that quantifies the impact of additive noise. This noise loudness density function is then integrated over the pitch to derive an average noise impact indicator  42  (NOISE). This indicator  42  is thus calculated from an ideal silence so that a transparent chain that is measured using a noisy reference signal will thus not provide the maximum MOS score in the final POLQA end-to-end speech quality measurement. 
     For the impact of room reverberations, the energy over time function (ETC) is calculated from the reference and degraded time series. The ETC represents the envelope of the impulse response. In a first step the loudest reflection is calculated by simply determining the maximum value of the ETC curve after the direct sound. In the POLQA model direct sound is defined as all sounds that arrive within 60 ms. Next a second loudest reflection is determined over the interval without the direct sound and without taking into account reflections that arrive within 100 ms from the loudest reflection. Then the third loudest reflection is determined over the interval without the direct sound and without taking into account reflections that arrive within 100 ms from the loudest and second loudest reflection. The energies of the three loudest reflections are then combined into a single reverb indicator  43  (REVERB). 
     Global and Local Scaling of the Reference Signal Towards the Degraded Signal (Step  26 ) 
     The reference signal is now in accordance with step  17  at the internal ideal level, i.e. about 73 dB SPL equivalent, while the degraded signal is represented at a level that coincides with the playback level as a result of  46 . Before a comparison is made between the reference and degraded signal the global level difference is compensated in step  26 . Furthermore small changes in local level are partially compensated to account for the fact that small enough level variations are not noticeable to subjects in a listening-only situation. The global level equalization  26  is carried out on the basis of the average power of reference and degraded signal using the frequency components between 400 and 3500 Hz. The reference signal is globally scaled towards the degraded signal and the impact of the global playback level difference is thus maintained at this stage of processing. Similarly, for slowly varying gain distortions a local scaling is carried out for level changes up to about 3 dB using the full bandwidth of both the reference and degraded speech file. 
     Partial Compensation of the Original Pitch Power Density for Linear Frequency Response Distortions (Step  27 ) 
     In order to correctly model the impact of linear frequency response distortions, induced by filtering in the system under test, a partial compensation approach is used in step  27 . To model the imperceptibility of moderate linear frequency response distortions in the subjective tests, the reference signal is partially filtered with the transfer characteristics of the system under test. This is carried out by calculating the average power spectrum of the original and degraded pitch power densities over all speech active frames. Per Bark bin, a partial compensation factor is calculated  27  from the ratio of the degraded spectrum to the original spectrum. 
     Modelling of Masking Effects, Calculation of the Pitch Loudness Density Excitation 
     Masking is modelled in steps  30  and  58  by calculating a smeared representation of the pitch power densities. Both time and frequency domain smearing are taken into account in accordance with the principles illustrated in  FIGS. 5 a  through 5 c   . The time-frequency domain smearing uses the convolution approach. From this smeared representation, the representations of the reference and degraded pitch power density are re-calculated suppressing low amplitude time-frequency components, which are partially masked by loud components in the neighbourhood in the time-frequency plane. This suppression is implemented in two different manners, a subtraction of the smeared representation from the non-smeared representation and a division of the non-smeared representation by the smeared representation. The resulting, sharpened, representations of the pitch power density are then transformed to pitch loudness density representations using a modified version of Zwicker&#39;s power law: 
                 LX   ⁡     (   f   )       n     =     SL   *       (         P   0     ⁡     (   f   )       0.5     )       0.22   *     f   B     *     P   fn         *     [         (     0.5   +     0.5   ⁢         PPX   ⁡     (   f   )       n         P   0     ⁡     (   f   )             )       0.22   *     f   B     *     P   fn         -   1     ]             
with SL the loudness scaling factor, P0(f) the absolute hearing threshold, fB and Pfn a frequency and level dependent correction defined by:
 
 f   B =−0.03* f+ 1.06 for  f&lt; 2.0 Bark
 
 f   B =1.0 for 2.0 ≦f≦ 22 Bark
 
 f   B =−0.2*( f− 22.0)+1.0 for  f&gt; 22.0 Bark
 
 P   fn =( PPX ( f ) n +600) 0.008  
 
with f representing the frequency in Bark, PPX(f) n  the pitch power density in frequency time cell f, n. The resulting two dimensional arrays LX(f) n  and LY(f) n  are called pitch loudness densities, at the output of step  30  for the reference signal X(t) and step  58  for the degraded signal Y(t) respectively.
 
     Global Low Level Noise Suppression in Reference and Degraded Signals 
     Low levels of noise in the reference signal, which are not affected by the system under test (e.g., a transparent system) will be attributed to the system under test by subjects due to the absolute category rating test procedure. These low levels of noise thus have to be suppressed in the calculation of the internal representation of the reference signal. This “idealization process” is carried out in step  33  by calculating the average steady state noise loudness density of the reference signal LX(f) n  over the super silent frames as a function of pitch. This average noise loudness density is then partially subtracted from all pitch loudness density frames of the reference signal. The result is an idealized internal representation of the reference signal, at the output of step  33 . 
     Steady state noise that is audible in the degraded signal has a lower impact than non-steady state noise. This holds for all levels of noise and the impact of this effect can be modelled by partially removing steady state noise from the degraded signal. This is carried out in step  60  by calculating the average steady state noise loudness density of the degraded signal LY(f) n  frames for which the corresponding frame of the reference signal is classified as super silent, as a function of pitch. This average noise loudness density is then partially subtracted from all pitch loudness density frames of the degraded signal. The partial compensation uses a different strategy for low and high levels of noise. For low levels of noise the compensation is only marginal while the suppression that is used becomes more aggressive for loud additive noise. The result is an internal representation  61  of the degraded signal with an additive noise that is adapted to the subjective impact as observed in listening tests using an idealized noise free representation of the reference signal. 
     In the present embodiment, in step  33  above, in addition to performing the global low level noise suppression, also the LOUDNESS indicator  32  is determined for each of the reference signal frames, in accordance with the present invention. The LOUDNESS indicator or LOUDNESS value will be used to determine a loudness dependent weighting factor for weighing specific types of distortions. The weighing itself may be implemented in steps  125  and  125 ′ for the four representations of distortions provided by operators  7 ,  8 ,  9  and  10 , upon providing the final disturbance densities  142  and  143 . 
     Here, the loudness level indicator has been determined in step  33 , but one may appreciate that the loudness level indicator may be determined for each reference signal frame in another part of the method. In step  33  determining the loudness level indicator is possible due to the fact that already the average steady state noise loud density is determined for reference signal LX(f) n  over the super silent frames, which are then used in the construction of the noise free reference signal for all reference frames. However, although it is possible to implement this in step  33 , it is not the most preferred manner of implementation. 
     Alternatively, the loudness level indicator (LOUDNESS) may be taken from the reference signal in an additional step following step  35 . This additional step is also indicated in  FIG. 1  as a dotted box  35 ′ with dotted line output (LOUDNESS)  32 ′. If implemented there in step  35 ′, it is no longer necessary to take the loudness level indicator from step  33 , as the skilled reader may appreciate. 
     Local Scaling of the Distorted Pitch Loudness Density for Time-Varying Gain Between Degraded and Reference Signal (Steps  34  and  63 ) 
     Slow variations in gain are inaudible and small changes are already compensated for in the calculation of the reference signal representation. The remaining compensation necessary before the correct internal representation can be calculated is carried out in two steps; first the reference is compensated in step  34  for signal levels where the degraded signal loudness is less than the reference signal loudness, and second the degraded is compensated in step  63  for signal levels where the reference signal loudness is less than the degraded signal loudness. 
     The first compensation  34  scales the reference signal towards a lower level for parts of the signal where the degraded shows a severe loss of signal such as in time clipping situations. The scaling is such that the remaining difference between reference and degraded represents the impact of time clips on the local perceived speech quality. Parts where the reference signal loudness is less than the degraded signal loudness are not compensated and thus additive noise and loud clicks are not compensated in this first step. 
     The second compensation  63  scales the degraded signal towards a lower level for parts of the signal where the degraded signal shows clicks and for parts of the signal where there is noise in the silent intervals. The scaling is such that the remaining difference between reference and degraded represents the impact of clicks and slowly changing additive noise on the local perceived speech quality. While clicks are compensated in both the silent and speech active parts, the noise is compensated only in the silent parts. 
     Partial Compensation of the Original Pitch Loudness Density for Linear Frequency Response Distortions (Step  35 ) 
     Imperceptible linear frequency response distortions were already compensated by partially filtering the reference signal in the pitch power density domain in step  27 . In order to further correct for the fact that linear distortions are less objectionable than non-linear distortions, the reference signal is now partially filtered in step  35  in the pitch loudness domain. This is carried out by calculating the average loudness spectrum of the original and degraded pitch loudness densities over all speech active frames. Per Bark bin, a partial compensation factor is calculated from the ratio of the degraded loudness spectrum to the original loudness spectrum. This partial compensation factor is used to filter the reference signal with smoothed, lower amplitude, version of the frequency response of the system under test. After this filtering, the difference between the reference and degraded pitch loudness densities that result from linear frequency response distortions is diminished to a level that represents the impact of linear frequency response distortions on the perceived speech quality. 
     Final Scaling and Noise Suppression of the Pitch Loudness Densities 
     Up to this point, all calculations on the signals are carried out on the playback level as used in the subjective experiment. For low playback levels, this will result in a low difference between reference and degraded pitch loudness densities and in general in a far too optimistic estimation of the listening speech quality. In order to compensate for this effect the degraded signal is now scaled towards a “virtual” fixed internal level in step  64 . After this scaling, the reference signal is scaled in step  36  towards the degraded signal level and both the reference and degraded signal are now ready for a final noise suppression operation in  37  and  65  respectively. This noise suppression takes care of the last parts of the steady state noise levels in the loudness domain that still have a too big impact on the speech quality calculation. The resulting signals  13  and  14  are now in the perceptual relevant internal representation domain and from the ideal pitch-loudness-time LX ideal (f) n    13  and degraded pitch-loudness-time LY deg (f) n    14  functions the disturbance densities  142  and  143  can be calculated. Four different variants of the ideal and degraded pitch-loudness-time functions are calculated in  7 ,  8 ,  9  and  10 , two variants ( 7  and  8 ) focussed on the disturbances for normal and big distortions, and two ( 9  and  10 ) focussed on the added disturbances for normal and big distortions. 
     Calculation of the Final Disturbance Densities 
     Two different flavours of the disturbance densities  142  and  143  are calculated. The first one, the normal disturbance density, is derived in  7  and  8  from the difference between the ideal pitch-loudness-time LX ideal (f) n  and degraded pitch-loudness-time function LY deg (f) n . The second one is derived in  9  and  10  from the ideal pitch-loudness-time and the degraded pitch-loudness-time function using versions that are optimized with regard to introduced degradations and is called added disturbance. In this added disturbance calculation, signal parts where the degraded power density is larger than the reference power density are weighted with a factor dependent on the power ratio in each pitch-time cell, the asymmetry factor. 
     In order to be able to deal with a large range of distortions two different versions of the processing are carried out, one focussed on small to medium distortions based on 7 and 9 and one focussed on medium to big distortions based on 8 and 10. The switching between the two is carried out on the basis of a first estimation from the disturbance focussed on small to medium level of distortions. This processing approach leads to the necessity of calculating four different ideal pitch-loudness-time functions and four different degraded pitch-loudness-time functions in order to be able to calculate a single disturbance and a single added disturbance function (see  FIG. 3 ) which are then compensated for a number of different types of severe amounts of specific distortions. 
     Severe deviations of the optimal listening level are quantified in  127  and  127 ′ by an indicator directly derived from the signal level of the degraded signal. This global indicator (LEVEL) is also used in the calculation of the MOS-LQO. 
     Severe distortions introduced by frame repeats are quantified  128  and  128 ′ by an indicator derived from a comparison of the correlation of consecutive frames of the reference signal with the correlation of consecutive frames of the degraded signal. 
     Severe deviations from the optimal “ideal” timbre of the degraded signal are quantified  129  and  129 ′ by an indicator derived from the ratio of the upper frequency band loudness and the lower frequency band loudness. Compensations are carried out per frame and on a global level. This compensation calculates the power in the lower and upper Bark bands (below 12 and above 7 Bark, i.e. using a 5 Bark overlap) of the degraded signal and “punishes” any severe imbalance irrespective of the fact that this could be the result of an incorrect voice timbre of the reference speech file. Note that a transparent chain using poorly recorded reference signals, containing too much noise and/or an incorrect voice timbre, will thus not provide the maximum MOS score in a POLQA end-to-end speech quality measurement. This compensation also has an impact when measuring the quality of devices which are transparent. When reference signals are used that show a significant deviation from the optimal “ideal” timbre the system under test will be judged as non-transparent even if the system does not introduce any degradation into the reference signal. 
     The impact of severe peaks in the disturbance is quantified in  130  and  130 ′ in the FLATNESS indicator which is also used in the calculation of the MOS-LQO. 
     Severe noise level variations which focus the attention of subjects towards the noise are quantified in  131  and  131 ′ by a noise contrast indicator derived from the silent parts of the reference signal. 
     In steps  133  and  133 ′, in accordance with the invention, a weighting operation is performed for weighing disturbances dependent on whether or not they coincide with the actual spoken voice. In order to assess the intelligibility of the degraded signal, disturbances which are perceived during silent periods are not considered to be as detrimental as disturbances which are perceived during actual spoken voice. Therefore, in accordance with the invention, based on the LOUDNESS indicator determined in step  33  (or step  35 ′ in the alternative embodiment) from the reference signal, a weighting value is determined for weighing any disturbances. The weighting value is used for weighing the difference function (i.e. disturbances) for incorporating the impact of the disturbances on the intelligibility of the degraded speech signal into the evaluation. In particular, since the weighting value is determined based on the LOUDNESS indicator, the weighting value may be represented by a loudness dependent function. In the present embodiment, the loudness dependent weighting value is determined by comparing the loudness value to a threshold. If the loudness indicator exceeds the threshold the perceived disturbances are fully taken in consideration when performing the evaluation. On the other hand, if the loudness value is smaller than the threshold, the weighting value is made dependent on the loudness level indicator; i.e. in the present embodiment the weighting value is equal to the loudness level indicator (in the regime where LOUDNESS is below the threshold). The advantage is that for weak parts of the speech signal, e.g. at the ends of spoken words just before a pause or silence, disturbances are taken partially into account as being detrimental to the intelligibility. As an example, one may appreciate that a certain amount of noise perceived while speaking out the letter ‘f’ at the end of a word, may cause a listener to perceive this as being the letter ‘s’. This could be detrimental to the intelligibility. On the other hand, the skilled person may appreciate that it is also possible (in a different embodiment) to simply disregard any noise during silence or pauses, by turning the weighting value to zero when the loudness value is below the above mentioned threshold. The method of weighing the disturbance in a loudness dependent manner is further described below in relation to  FIG. 6 . 
     In addition to the above the method proposed can be further extended to take into account the fact that disturbances which are perceived during the pronunciation of vowels in a speech signal are not as detrimental as disturbances which are perceived during consonants. Analysis of the power envelope of a speech signal reveals that generally, the loudness of the signal during pronunciation of a vowel represents a local maximum, while during pronunciation of consonants the loudness is usually at an intermediate level. Disturbances during pronunciation of a consonant have more impact on speech intelligibility than disturbances during vowels where the signal power is strong enough for the observer to identify the vowel. Therefore, as a further improvement, the loudness value may be compared to two thresholds. Comparison of the loudness with the first threshold will cause the system to operate as indicated above; i.e. the loudness being below the first threshold will make the weighting value smaller than a maximum value and dependent on the loudness, while exceeding the first threshold causes the weighting value to be set to the maximum (e.g. 1.0 for fully taking the disturbance into account). Comparison of the loudness with the second threshold will cause the system to operate as follows. If the loudness is below the second threshold, the weighting value will be smaller than a maximum value and dependent on the loudness. If the loudness exceeds the first threshold, the weighting value is set to a maximum value. This embodiment of the method of weighing disturbance is illustrated in  FIG. 7 . Proceeding again with  FIG. 3 , severe jumps in the alignment are detected in the alignment and the impact is quantified in steps  136  and  136 ′ by a compensation factor. 
     Finally the disturbance and added disturbance densities are clipped in  137  and  137 ′ to a maximum level and the variance of the disturbance  138  and  138 ′ and the impact of jumps  140  and  140 ′ in the loudness of the reference signal are used to compensate for specific time structures of the disturbances. 
     This yields the final disturbance density D(f) n    142  for regular disturbance and the final disturbance density DA(f) n    143  for added disturbance. 
     Aggregation of the Disturbance over Pitch, Spurts and Time, Mapping to Intermediate MOS Score 
     The final disturbance D(f) n    142  and added disturbance DA(f) n  densities  143  are integrated per frame over the pitch axis resulting in two different disturbances per frame, one derived from the disturbance and one derived from the added disturbance, using an L 1  integration  153  and  159  (see  FIG. 4 ): 
               D   n     =       ∑       f   =   1     ,     …   ⁢           ⁢   Number   ⁢           ⁢   of   ⁢           ⁢   Barkbands                 ⁢              D   ⁡     (   f   )       n          ⁢     W   f                       DA   n     =       ∑       f   =   1     ,     …   ⁢           ⁢   Number   ⁢           ⁢   of   ⁢           ⁢   Barkbands                 ⁢              DA   ⁡     (   f   )       n          ⁢     W   f               
with Wf a series of constants proportional to the width of the Bark bins.
 
     Next these two disturbances per frame are averaged over speech spurts of six consecutive frames with an L 4    155  and an L 1    160  weighing for the disturbance and for the added disturbance, respectively. 
     
       
         
           
             
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               n 
             
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     Finally a disturbance and an added disturbance are calculated per file from an L 2    156  and  161  averaging over time: 
     
       
         
           
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                   numberOfFrames 
                 
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                     DS 
                     n 
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     The added disturbance is compensated in step  161  for loud reverberations and loud additive noise using the REVERB  42  and NOISE  43  indicators. The two disturbances are then combined  170  with the frequency indicator  41  (FREQ) to derive an internal indicator that is linearized with a third order regression polynomial to get a MOS like intermediate indicator  171 . 
     Computation of the Final POLQA MOS-LQO 
     The raw POLQA score is derived from the MOS like intermediate indicator using four different compensations all in step  175 : 
     two compensations for specific time-frequency characteristics of the disturbance, one calculated with an L 511  aggregation over frequency  148 , spurts  149  and time  150 , and one calculated with an L 313  aggregation over frequency  145 , spurts  146  and time  147   
     one compensation for very low presentation levels using the LEVEL indicator 
     one compensation for big timbre distortions using the FLATNESS indicator 
     The training of this mapping is carried out on a large set of degradations, including degradations that were not part of the POLQA benchmark. These raw MOS scores  176  are for the major part already linearized by the third order polynomial mapping used in the calculation of the MOS like intermediate indicator  171 . 
     Finally the raw POLQA MOS scores  176  are mapped in  180  towards the MOS-LQO scores  181  using a third order polynomial that is optimized for the 62 databases as were available in the final stage of the POLQA standardization. In narrowband mode the maximum POLQA MOS-LQO score is 4.5 while in super-wideband mode this point lies at 4.75. An important consequence of the idealization process is that under some circumstances, when the reference signal contains noise or when the voice timbre is severely distorted, a transparent chain will not provide the maximum MOS score of 4.5 in narrowband mode or 4.75 in super-wideband mode. 
       FIG. 6  illustrates an overview of a method of weighing the disturbance or noise with respect to the loudness value in accordance with the present invention. Although the method as illustrated in  FIG. 6  only focuses on the relevant parts relating to determining the loudness value and performing the weighing of disturbances, it will be appreciated that this method can be incorporated as part of an evaluation method as described in this document, or an alternative thereof. 
     In step  222 , a loudness value is determined for each frame of the reference signal  220 . This step may be implemented in step  33  of  FIG. 1 , or as described above in step  35 ′ also depicted in  FIG. 1  as a preferred alternative. The skilled person may appreciate that the loudness value may be determined somewhere else in the method, provided that the loudness value is timely available upon performing the weighing. 
     In step  225 , the loudness value determined in step  222  is compared to a threshold  226 . The outcome of this comparison may either be that the loudness value is larger than the threshold  226 , in which case the method continues via of  228 ; or that the loudness value may be smaller than the threshold  226 , in which case the method continues through path  231 . 
     If the loudness value is larger than the threshold (path  228 ), in step  230  the loudness dependent weighting factor is determined. In the present embodiment, the weighting factor is set at 1.0 in order to fully take into account the disturbance in the degraded signal. The skilled person will appreciate that the situation where the loudness value is larger than the threshold corresponds to the speech signal carrying information at the present time (the reference signal frame coincides with the actual words being spoken). The invention is not limited to a weighting factor of 1.0 in the abovementioned situation; the skilled person may opt to use any other value or dependency deemed suitable for a given situation. The invention primarily focuses on making a distinction between disturbances encountered during speech and disturbances encountered during (almost) silent periods, en treating the disturbances differently in both regimes. 
     In case the loudness value is smaller than the threshold and the method continues through path  231 , in step  233  the weighting value is determined by setting the weighting factor as being dependent on the loudness value. Good results have been experienced by directly using the loudness value as weighting factor. However any suitable dependency may be applied, i.e. linear, quadratic, a polynomial of any suitable order, or another dependency. The weighting factor must be smaller than 1.0 as will be appreciated. 
     As an alternative to the above described loudness dependent weighting factor, it is also possible to include the frequency dependency of the loudness in the method of the present invention. In that case, the weighting factor will not only be dependent on the loudness, but also on the frequency of the disturbance in the speech signal. 
     The weighting factor determined in either one of steps  230  and  233  is used as an input value  235  for weighing the importance of disturbances in step  240  as a function of whether or not the degraded signal actually carries spoken voice at the present frame. In step  240 , the difference signal  238  is received and the weighting factor  235  is applied for providing the desired output (OUT). 
       FIG. 7  illustrates an overview of a further embodiment of a method of weighing the disturbance or noise with respect to the loudness value in accordance with the present invention. In view of similarities between  FIGS. 6 and 7 , in  FIG. 7  same reference signs have been used as in  FIG. 6  for elements and steps of the method that are similar or equivalent to the method described in  FIG. 6 . Again, the method as illustrated in  FIG. 7  only focuses on the relevant parts relating to determining the loudness value and performing the weighing of disturbances, but it will be appreciated that this method can be incorporated as part of an evaluation method as described in this document, or an alternative thereof. 
     In step  222 , a loudness value is determined for each frame of the reference signal  220 . This step may be implemented in step  33  of  FIG. 1 , or as described above in step  35 ′ also depicted in  FIG. 1  as a preferred alternative. The skilled person may appreciate that the loudness value may be determined somewhere else in the method, provided that the loudness value is timely available upon performing the weighing. 
     In step  225 , the loudness value determined in step  222  is compared to a first threshold  226 . The outcome of this comparison may either be that the loudness value is larger than the first threshold  226 , in which case the method continues via of  228 ; or that the loudness value may be smaller than the first threshold  226 , in which case the method continues through path  231 . 
     If the loudness value is larger than the first threshold (path  228 ), in step  242 , the loudness value is compared to a second threshold  243 . The second threshold  243  is larger than the first threshold  226 . The outcome of this comparison may either be that the loudness value is larger than the second threshold  243 , in which case the method continues via of  245 ; or that the loudness value may be smaller than the threshold  243 , in which case the method continues through path  248 . 
     If the loudness value is smaller than the second threshold  243  (path  248 ), in step  249  the loudness dependent weighting factor is determined. In the present embodiment, the weighting factor is set at 1.0 (a maximum value) in order to fully take into account the disturbance in the degraded signal. The skilled person will appreciate that the situation where the loudness value is larger than the threshold corresponds to the speech signal during pronunciation of a vowel; i.e. a local maximum in the power envelope. The invention is not limited to a weighting factor of 1.0 in the abovementioned situation; the skilled person may opt to use any other value or dependency deemed suitable for a given situation. In this embodiment, the invention focuses on making a distinction between disturbances encountered during speech and disturbances encountered during (almost) silent periods. Moreover, where disturbance is encountered during speech, this embodiment further focuses on making a distinction between disturbance encountered during pronunciation of vowels and disturbance encountered during pronunciation of consonants. The disturbances are treated differently in each of these regimes. 
     In case the loudness value is larger than the second threshold  243  and the method continues through path  245 , in step  246  the weighting value is determined by setting the weighting factor as being dependent on the loudness value. Good results have been experienced by making the weighing factor dependent in the following manner:
 
weighting value=(loudness−2 nd  threshold+1.0) −1*q  
 
wherein the power factor q may be equal to any desired value. Good results were obtained with q=0.3.
 
     Instead of the above relation, any suitable dependency may be applied, i.e. linear, quadratic, a polynomial of any suitable order, or another dependency. The weighting factor must be smaller than the maximum value 1.0 as will be appreciated. 
     As an alternative to the above described loudness dependent weighting factor, it is also possible to include the frequency dependency of the loudness in the method of the present invention. In that case, the weighting factor will not only be dependent on the loudness, but also on the frequency of the disturbance in the speech signal. 
     The weighting factor determined in either one of steps  233 ,  246  or  249  is used as an input value  235  for weighing the importance of disturbances in step  240  as a function of whether or not the degraded signal actually carries spoken voice at the present frame. In step  240 , the difference signal  238  is received and the weighting factor  235  is applied for providing the desired output (OUT). The invention may be practised differently than specifically described herein, and the scope of the invention is not limited by the above described specific embodiments and drawings attached, but may vary within the scope as defined in the appended claims. 
     REFERENCE SIGNS 
     
         
           3  reference signal X(t) 
           5  degraded signal Y(t), amplitude-time 
           7  difference calculation 
           8  first variant of difference calculation 
           9  second variant of difference calculation 
           10  third variant of difference calculation 
           12  difference signal 
           13  internal ideal pitch-loudness-time LX ideal   (f)   n    
           14  internal degraded pitch-loudness-time LY deg   (f)   n    
           17  global scaling towards fixed level 
           18  windowed FFT 
           20  scaling factor SP 
           21  warp to Bark 
           25  (super) silent frame detection 
           26  global &amp; local scaling to degraded level 
           27  partial frequency compensation 
           30  excitation and warp to sone 
           31  absolute threshold scaling factor SL 
           32  LOUDNESS 
           32 ′ LOUDNESS (determined according to alternative step  35 ′) 
           33  global low level noise suppression 
           34  local scaling if Y&lt;X 
           35  partial frequency compensation 
           35 ′ (alternative) determine loudness 
           36  scaling towards degraded level 
           37  global low level noise suppression 
           40  FREQ NOISE REVERB indicators 
           41  FREQ indicator 
           42  NOISE indicator 
           43  REVERB indicator 
           44  PW_R overall  indicator (overall audio power ratio between degr. and ref. signal) 
           45  PW_R frame  indicator (per frame audio power ratio between degr. and ref. signal) 
           46  scaling towards playback level 
           47  calibration factor C 
           49  windowed FFT 
           52  frequency align 
           54  warp to Bark 
           55  scaling factor SP 
           56  degraded signal pitch-power-time PPY (f)   n    
           58  excitation and warp to sone 
           59  absolute threshold scaling factor SL 
           60  global high level noise suppression 
           61  degraded signal pitch-loudness-time 
           63  local scaling if Y&gt;X 
           64  scaling towards fixed internal level 
           65  global high level noise suppression 
           70  reference spectrum 
           72  degraded spectrum 
           74  ratio of ref and deg pitch of current and +/−1 surrounding frame 
           77  preprocessing 
           78  smooth out narrow spikes and drops in FFT spectrum 
           79  take log of spectrum, apply threshold for minimum intensity 
           80  flatten overall log spectrum shape using sliding window 
           83  optimization loop 
           84  range of warping factors: [min pitch ratio&lt;=1&lt;=max pitch ratio] 
           85  warp degraded spectrum 
           88  apply preprocessing 
           89  compute correlation of spectra for bins &lt;1500 Hz 
           90  track best warping factor 
           93  warp degraded spectrum 
           94  apply preprocessing 
           95  compute correlation of spectra for bins &lt;3000 Hz 
           97  keep warped degraded spectrum if correlation sufficient restore original otherwise 
           98  limit change of warping factor from one frame to the next 
           100  ideal regular 
           101  degraded regular 
           104  ideal big distortions 
           105  degraded big distortions 
           108  ideal added 
           109  degraded added 
           112  ideal added big distortions 
           113  degraded added big distortions 
           116  disturbance density regular select 
           117  disturbance density big distortions select 
           119  added disturbance density select 
           120  added disturbance density big distortions select 
           121  PW_R overall  input to switching function  123   
           122  PW_R frame  input to switching function  123   
           123  big distortion decision (switching) 
           125  correction factors for severe amounts of specific distortions 
           125 ′ correction factors for severe amounts of specific distortions 
           127  level 
           127 ′ level 
           128  frame repeat 
           128 ′ frame repeat 
           129  timbre 
           129 ′ timbre 
           130  spectral flatness 
           130 ′ spectral flatness 
           131  noise contrast in silent periods 
           131 ′ noise contrast in silent periods 
           133  loudness dependent disturbance weighing 
           133 ′ loudness dependent disturbance weighing 
           134  Loudness of reference signal 
           134 ′ Loudness of reference signal 
           136  align jumps 
           136 ′ align jumps 
           137  clip to maximum degradation 
           137 ′ clip to maximum degradation 
           138  disturbance variance 
           138 ′ disturbance variance 
           140  loudness jumps 
           140 ′ loudness jumps 
           142  final disturbance density D (f)   n    
           143  final added disturbance density DA (f)   n    
           145  L 3  frequency integration 
           146  L 1  spurt integration 
           147  L 3  time integration 
           148  L 5  frequency integration 
           149  L 1  spurt integration 
           150  L 1  time integration 
           153  L 1  frequency integration 
           155  L 4  spurt integration 
           156  L 2  time integration 
           159  L 1  frequency integration 
           160  L 1  spurt integration 
           161  L 2  time integration 
           170  mapping to intermediate MOS score 
           171  MOS like intermediate indicator 
           175  MOS scale compensations 
           176  raw MOS scores 
           180  mapping to MOS-LQO 
           181  MOS LQO 
           185  Intensity over time for short sinusoidal tone 
           187  short sinusoidal tone 
           188  masking threshold for a second short sinusoidal tone 
           195  Intensity over frequency for short sinusoidal tone 
           198  short sinusoidal tone 
           199  making threshold for a second short sinusoidal tone 
           205  Intensity over frequency and time in 3D plot 
           211  masking threshold used as suppression strength leading to a sharpened internal representation 
           220  reference signal frames 
           222  determine LOUDNESS 
           225  compare LOUDNESS to THRESHOLD 
           226  (FIRST) THRESHOLD 
           228  LOUDNESS&gt;THRESHOLD 
           230  WEIGHTING FACTOR=1.0 
           231  LOUDNESS&lt;THRESHOLD 
           233  WEIGHTING FACTOR linear dependent on LOUDNESS 
           235  determined value for WEIGHTING VALUE 
           238  difference signal/disturbance 
           240  weighing step of disturbance 
           242  compare LOUDNESS to SECOND THRESHOLD 
           243  SECOND THRESHOLD 
           245  LOUDNESS&gt;SECOND THRESHOLD 
           246  WEIGHTING FACTOR linear dependent on LOUDNESS, e.g.:
 
WEIGHTING VALUE=(LOUDNESS−2 nd  THRESHOLD+1.0) −1*g  
 
       
    
     where q may be equal to 0.3.
       248  LOUDNESS&lt;SECOND THRESHOLD     249  WEIGHTING FACTOR=1.0