EP1244094A1 - Procédé et dispositif de détermination de la qualité d'un signal audio - Google Patents

Procédé et dispositif de détermination de la qualité d'un signal audio Download PDF

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Publication number
EP1244094A1
EP1244094A1 EP01810285A EP01810285A EP1244094A1 EP 1244094 A1 EP1244094 A1 EP 1244094A1 EP 01810285 A EP01810285 A EP 01810285A EP 01810285 A EP01810285 A EP 01810285A EP 1244094 A1 EP1244094 A1 EP 1244094A1
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Prior art keywords
signal
audio signal
quality
determining
interruptions
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EP01810285A
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German (de)
English (en)
Inventor
Pero Juric
Thomet Bendicht
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Rohde and Schwarz SwissQual AG
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SwissQual AG
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Application filed by SwissQual AG filed Critical SwissQual AG
Priority to EP01810285A priority Critical patent/EP1244094A1/fr
Priority to EP02703438.8A priority patent/EP1386307B2/fr
Priority to AT02703438T priority patent/ATE289109T1/de
Priority to US10/101,533 priority patent/US6804651B2/en
Priority to DE50202226T priority patent/DE50202226D1/de
Priority to PCT/CH2002/000164 priority patent/WO2002075725A1/fr
Publication of EP1244094A1 publication Critical patent/EP1244094A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/69—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for evaluating synthetic or decoded voice signals

Definitions

  • the invention relates to a method for determining a quality measure of an audio signal.
  • the invention further relates to a device for performing this method and a noise reduction module and an interrupt detection and interpolation module for use in such a device.
  • Assessing the quality of a telecommunications network is an important tool to achieve or maintain a desired quality of service.
  • One way that Assessing the quality of service of a telecommunications network is quality to determine a signal transmitted over the telecommunications network.
  • audio signals Various intrusive methods are known for this, particularly in the case of voice signals.
  • the system under test is used intervened by occupying a transmission channel and transmitting a reference signal therein becomes.
  • the quality assessment is then carried out by comparing the known one Reference signal with the received signal, for example, subjectively by or a large number of test subjects.
  • this is complex and therefore expensive.
  • EP 0 980 064 describes a further intrusive method for machine-assisted quality assessment described an audio signal, being used to assess the transmission quality a spectral similarity value of the known source signal and the received signal is determined. This similarity value is based on a calculation of the covariance the spectra of the source signal and the received signal and a division of the covariance by the standard deviations of the two spectra mentioned.
  • intrusive methods generally have the disadvantage that, as already mentioned, this too testing system must be intervened. To determine the signal quality namely at least one transmission channel is occupied and a reference signal is transmitted therein become. This transmission channel cannot be used for data transmission during this time be used. It is also in a broadcasting system such as a broadcasting service in principle possible, the signal source for transmission of test signals, since this occupies all channels and the test signal this would be extremely impractical for all recipients. Intrusive Methods are also unsuitable to simultaneously control the quality of a variety of transmission channels to monitor.
  • the object of the invention is to provide a method of the type mentioned above, which avoids the disadvantages of the prior art and in particular offers a possibility to assess the signal quality of a transmitted over a telecommunications network Signal without knowing the originally sent signal.
  • a reference signal is first determined from the audio signal. through Comparing the determined reference signal with the audio signal becomes a quality value determined, which is used to determine the quality measure.
  • the method according to the invention thus allows an assessment of the quality of an audio signal at any connection of the telecommunication network. That is, it allows thus the quality assessment of many transmission channels at the same time, even a simultaneous assessment of all channels would be possible.
  • the quality assessment takes place solely on the basis of the properties of the received signal, d. H. without knowing the source signal or the signal source.
  • the invention thus not only enables monitoring of the transmission quality of the Telecommunications network, but also, for example, quality-based cost allocation, quality-based routing in the network, a test of the coverage ratio for example in the case of mobile radio networks, a QOS (Quality of Service) control of the network nodes or a quality comparison within a network or across networks.
  • QOS Quality of Service
  • Signal information typically also includes unwanted components such as different noise components, which are not in the original source signal were present.
  • the reference signal is determined by the in received signal estimated interference signal and then from the received signal can be removed.
  • a noisy audio signal is determined, which is preferred as Reference signal is used to assess the transmission quality.
  • the audio signal could, for example, be passed through appropriate filters.
  • a preferred method is to remove the noise components from the audio signal however, a neural network is used for this.
  • the audio signal is not used directly as an input signal.
  • DWT discrete wavelet transform
  • This transformation provides a plurality of DWT coefficients of the audio signal, which the neural Network as an input signal.
  • the neural network delivers on Output a plurality of corrected DWT coefficients, from which with the inverse DWT the reference signal is obtained. This corresponds to the noiseless version of the Audio signal.
  • the coefficients of the neural network must be set in this way be that to the DWT coefficients of a noisy input signal provides the DWT coefficients of the corresponding noiseless input signal.
  • the neural network In order to the neural network delivers the desired coefficients, it must first with a Set of corresponding noisy or noisy signal pairs trained become.
  • any other information will also be taken into account. This can be both information contained in the audio signal, as well as information about the transmission channel or the telecommunications network itself.
  • the quality of the received audio signal is determined by the quality of the Transmission influenced codec's (coder - decoder) influenced. It is difficult to do such Determine signal degradations, for example if the codec bit rates are too low part of the original signal information is lost. However, have too small Codec bit rates result in a change in the fundamental frequency (pitch) of the audio signal, which is why the course and dynamics of the fundamental frequency in the audio signal are advantageously examined becomes. Because such changes are easiest based on audio signal sections with vowels examined, signal components in the audio signal are preferred detected with vowels and then examined for pitch variations.
  • the received audio signal can namely not only have unwanted signal components, it can also partially on the way desired information has been lost. So can the received audio signal for example, have more or less long signal interruptions.
  • the received audio signal can include various types of audio signals. So it can contain, for example, speech, music, noise or even quiet signal components.
  • the quality assessment can of course be based on all or part of this Signal components take place. In a preferred variant of the invention, the assessment the signal quality, however, is limited to the speech signal components. With an audio discriminator the speech signal components are therefore first extracted from the audio signal and only these speech signal components for determining the quality measure, d. H. to Determination of the reference signal used. To determine the quality value is in In this case, of course, the determined reference signal does not match the received audio signal, but only compared with the extracted speech signal component.
  • the device according to the invention for machine-assisted determination of a quality measure an audio signal comprises first means for determining a reference signal the audio signal, second means for determining a quality value by means of comparisons the determined reference signal with the audio signal and third means for determination the quality measure taking into account the quality value.
  • the first means for determining a reference signal from the audio signal can be several Include modules. So is preferably a noise reduction module and / or a Interrupt detection and interpolation module provided.
  • noise signal components can be received Suppress audio signal. It includes the means to carry out those already described Wavelet transformations and the neural network to determine the new one DWT coefficients.
  • the interrupt detection and interpolation module has those Means on the one hand for the detection of signal interruptions in the audio signal and on the other hand for the polynomial interpolation of short and for model-based interpolation of medium-long signal interruptions are required. That determined so The reference signal thus corresponds to a noisy version of the received audio signal and typically only shows major signal interruptions.
  • the information about the signal interruptions of the audio signal is not only used to determine a better reference signal, they can also be used for determination of a better quality measure can be used.
  • the third means of determination of the quality measure are therefore preferably designed such that information can be taken into account via signal interruptions in the audio signal.
  • the device therefore advantageously has fourth means for determining information about codec-related Signal distortion.
  • codec-related Signal distortion include, for example, a vowel detection module, with which signal components with vowels can be detected in the audio signal. These vowel signal components are passed on to an assessment module, which is based on this Signal components Information about codec-related signal distortions determines which can also be used to assess the signal quality.
  • the third means are appropriate formed such that this information about the codec-related signal distortion can be taken into account when determining the quality measure.
  • the device therefore has, in particular, fifth means for extracting the method Speech signal components from the audio signal. Accordingly, to determine the Reference signal not the audio signal itself, but only its voice signal component noisy and examined for interruptions. Likewise, of course, the audio signal, but only compared its voice signal component with this reference signal. In order to the quality measure is only determined on the basis of the information in the Speech signal component, whereby the information from the remaining signal components is not taken into account become.
  • FIG. 1 shows a block diagram of the method according to the invention.
  • a Audio signal 1 determines a quality measure 2, which is also used for evaluation, for example of the telecommunications network used (not shown).
  • the audio signal 1 is understood here to mean that signal which is a receiver after transmission over the telecommunications network.
  • This audio signal 1 typically does not match the one sent by the (not shown) transmitter Signal coincides, because on the way from the transmitter to the receiver the transmission signal opens varied ways. For example, it goes through various modules such as voice encoders and decoders, multiplexers and demultiplexers or even voice enhancers and echo cancellers.
  • the transmission channel itself can also be a big one Influence the signal, which can take the form of interference, fading, Express transmission interruptions or interruptions, echo generation etc.
  • the audio signal 1 thus not only contains the desired signal components, i. H. the original Transmitted signal, but also unwanted interference signal components. It may also be that Signal components of the transmission signal are missing, i. H. lost during transmission are.
  • the assessment of the signal quality is not based on of the entire audio signal 1, but only based on the contained therein Speech portion.
  • the audio signal 1 is initially based on an audio discriminator 3 Speech signal components 4 examined. Found speech signal components 4 become further Processing passed, whereas other signal components such as music 5.1, breaks 5.2 or strong signal interference 5.3 sorted out and processed further or can be discarded.
  • the audio signal 1 piecewise, d. H. to pieces a each about 100 ms to 500 ms, passed to the audio discriminator 3. This breaks these pieces down further individual buffers of about 20 ms in length, processes these buffers and then arranges them in each case one of the signal groups to be distinguished: voice signal, music, pause or strong interference to.
  • the audio discriminator 3 is used, for example, to assess the signal pieces an LPC (linear predictive coding) transformation, with which the coefficients of a adaptive filters corresponding to the human speech tract are calculated.
  • LPC linear predictive coding
  • the assignment of the signal pieces to the different signal groups is based on the Form of the transmission characteristics of this filter.
  • this speech signal component 4 now a reference signal 6, i. H. a best possible estimate of the original from the transmitter transmitted transmission signal, determined.
  • This reference signal estimation is carried out in several stages.
  • a noise suppression module 7 undesirable components are initially created Signal components such as stationary noise or impulse interference from the voice signal component 4 removed or suppressed. This is done with the help of a neural network, which was previously used as an input and a plurality of noisy signals each trained the corresponding noise-free version of the input signal as the target signal has been.
  • the noise-free speech signal 11 obtained in this way is sent to the second stage forwarded.
  • the interrupt detection and interpolation module 8 there are interruptions detected in the audio signal 1 or in its speech signal component 4 and if possible interpolated, d. H. the missing samples are replaced by suitably estimated values.
  • signal interruptions are detected by means of an examination discontinuities in the fundamental signal frequency (pitch tracing).
  • the interpolation is carried out depending on the length of the detected break.
  • d With short Interruptions, d. H. Interruptions of a few samples in length become a polynomial Interpolation such as a Lagrangian, Newton, Hermite, or Cubic Spline interpolation applied.
  • model-based interpolations such as a maximum a posteriori, an autoregressive or a frequency-time interpolation is used. With longer ones Signal interruptions is an interpolation or other signal reconstruction in generally no longer possible in a meaningful way.
  • an algorithm can be used for this comparison can be used as it is for example in intrusive methods for comparison of the known source signal is used with the received signal. Suitable are, for example, psychoacoustic models, the signals are perceptual, i. H. perceptible to compare.
  • the result of this comparison is an intrusive quality value of 10.
  • this intrusive quality value 10 becomes the input signals, ie the Speech signal component 4 and the reference signal 6, in signal pieces of about 20 to 30 ms Length broken down and a partial quality value calculated for each signal piece. After about 20 to 30 signal pieces, which corresponds to a signal duration of 0.5 seconds, becomes intrusive Quality value 10 determined as the arithmetic mean of these partial quality values.
  • the intrusive quality value 10 forms the output signal of the comparison module 9.
  • a speech encoder or speech decoder which the transmitted signal passes on its way from the transmitter to the receiver has an influence on the audio signal 1.
  • These influences exist, for example in that both the fundamental frequency and the frequencies of the higher harmonics of the signal vary. The lower the bit rate of the speech codecs used, the greater the frequency shifts and thus the signal distortions.
  • noisy speech signal 11 is first supplied to a vocal detector 12.
  • Vowel signals 13, i.e. H. Signal components which recognizes the neural network as vowels are forwarded to an evaluation module 14, other signal components are rejected.
  • the evaluation module 14 divides the vowel signal 13 into pieces of approximately 30 msec calculates a DFT (discrete Fourier transformation) with a frequency resolution of approximately 2 Hz at a sampling frequency of approximately 8 kHz. Leave with it then determine the fundamental frequency as well as the frequencies of the higher harmonics and examine for variations. Another feature to evaluate the codec-related Distortion forms the dynamics of the signal spectrum, with a smaller dynamic poor signal quality means.
  • the reference values for the dynamic evaluation are obtained from the sample signals for the individual vowels. From the information on the influence of codecs on frequency shifts and spectrum dynamics the audio signal 1 or the noisy speech signal 11 becomes a codec quality value 15 derived.
  • This value includes information about the length and the number of interruptions detected by the interruption detection and interpolation module 8, in a preferred embodiment of the invention only the information about the long breaks.
  • further quality information 18 about the received audio signal 1 or the noisy speech signal 11, which is determined with other modules or examinations are included in the calculations of quality measure 2.
  • the individual quality values are now scaled so that they are between 0 and 1 lie, with a quality value of 1 an undiminished quality and Values below 1 indicate a correspondingly reduced quality.
  • the quality measure 2 is finally calculated as a linear combination of the individual quality values, whereby the individual weighting coefficients are determined experimentally and determined in such a way that their sum is 1.
  • figure 2 shows the noise reduction module 7.
  • the speech signal component 4 of the audio signal 1 is first subjected to a known DWT 19 (discrete wavelet transformation). Similar to DFTs, DWTs are used for signal analysis. An essential one The difference is, however, in contrast to those used in a DFT, unlimited in time and thus sine or cosine waveforms not temporally localized, the use of so-called wavelets, i.e. H. limited and thus localized Average 0 waveforms.
  • the speech signal component 4 is divided into signal pieces of approximately 20 ms to 30 ms, which are each subject to DWT 19.
  • the result of DWT 19 is a set of DWT coefficients 20.1, which are fed as input vector to a neural network 20 become. Its coefficients have previously been trained to match a given one Set of DWT coefficients 20.1 of a noisy signal a new set of DWT coefficients 20.2 deliver the noiseless version of this signal.
  • This new set of DWT coefficient 20.2 is now the IDWT 21, i. H. subject to the DWT inverse to DWT 19. In this way, this IDWT 21 delivers a largely noiseless version of the Speech signal components 4, the desired, noiseless speech signal 11.
  • the training configuration of the neural network 20 is shown in FIG. 3. It will be with Trained pairs of noisy and noiseless versions of sample signals.
  • noisy example signal 22.1 is subjected to DWT 19 and it becomes a first one Obtained set 20.3 of DWT coefficients.
  • the noisy example signal 22.2 is also subjected to the same DWT 19 and a second set 20.4 of DWT coefficients generated, which is fed into the neural network 20.
  • the output vector of the neural Network 20, the new DWT coefficients 20.5, is in a comparator 23 with the first Theorem 20.3 of DWT coefficients compared. Because of the differences between These two sets of DWT coefficients are corrected 24 for the coefficients of the neural network 20.
  • This process is done with a variety of sample signal pairs repeated so that the coefficients of the neural network 20 perform the desired function perform more and more precisely.
  • the neural Network 20 uses sample signals 22.1, 22.2, which human sounds from different Represent languages. It is also an advantage to do this for both women and women Use men's and children's voices.
  • the mentioned size of the to be processed individually Signal pieces of 20 ms to 30 ms duration is selected so that the processing of the Speech signal portion 4 are carried out regardless of the language and the speaker can. Even pauses in speech and very quiet signal sections are trained with this these are also recognized correctly.
  • a multilayer perceptron was used as the neural network 20 with an input layer 25, a hidden layer 26 and one Output layer 27 used.
  • the perceptron was trained with a back propagation algorithm.
  • the input layer 25 has a plurality of input neurons 25.1, the hidden layer 26 a plurality of hidden neurons 26.1 and the Output layer 27 has a plurality of output neurons 27.1. Any input neuron 25.1 becomes one of the DWT coefficients 20.1 of the previous DWT 19 fed.
  • the respective values are determined with the set coefficients of the respective neurons and the value combinations are calculated in the individual neurons each output neuron 27.1 one of the new DWT coefficients 20.2.
  • the audio discriminator 3 divides the signal pieces into individual buffers Length 20 ms. At a sampling rate of 8 kHz, this corresponds to 160 samples.
  • a neural network 20 with 160 input and output neurons each can be used 25.1, 27.1 and about 50 to 60 hidden neurons 26.1 can be used.
  • time-frequency interpolation is used for signal reconstruction.
  • a short-term spectrum for signal frames with 64 samples Length (8 ms) calculated. This is done by placing the signal frames with Hamming windows be multiplied at an overlap of 50%.
  • the goal of interpolation is to treat this gap.
  • FIG. 5 shows such a signal 28 of approximately 200 samples in length.
  • About periodicity 5 shows the signal 28 in the temporal domain.
  • On the abscissa axis 32 is the number of samples and the ordinate axis 33 the magnitudes applied.
  • the interpolation takes place in the frequency-time domain.
  • the interruption 29 is easy to recognize as a gap of just under 10 samples in length.
  • the pitch period 30 of the signal 28 is first of all determined.
  • the interpolation will be information from the samples before and after the gap within this pitch period 30 is taken into account.
  • the signal areas 31.1, 31.2 show those Areas of the signal 28 a pitch period before or after the interruption 29.
  • This Signal areas 31.1, 31.2 are not identical to the original signal piece at break 29, but still show a high degree of similarity. For little ones Gaps up to about 10 samples are assumed to have enough signal information is present in order to be able to carry out correct interpolation. With longer gaps additional information from samples of the environment can be used.
  • the invention allows the signal quality of a judge received audio signal without knowing the original broadcast signal.
  • the signal quality can also affect the quality of the transmission channels used and thus concluded on the quality of service of the entire telecommunications network become.
  • the fast response times of the method according to the invention which are in the order of magnitude of approximately 100 ms to 500 ms, thus making different possible Applications such as general comparisons of the service quality of different ones Networks or subnetworks, a quality-based cost allocation or a quality-based one Routing in a network or across multiple networks using appropriate Control of the network nodes (gateways, routers etc.).
EP01810285A 2001-03-20 2001-03-20 Procédé et dispositif de détermination de la qualité d'un signal audio Withdrawn EP1244094A1 (fr)

Priority Applications (6)

Application Number Priority Date Filing Date Title
EP01810285A EP1244094A1 (fr) 2001-03-20 2001-03-20 Procédé et dispositif de détermination de la qualité d'un signal audio
EP02703438.8A EP1386307B2 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio
AT02703438T ATE289109T1 (de) 2001-03-20 2002-03-19 Verfahren und vorrichtung zur bestimmung eines qualitätsmasses eines audiosignals
US10/101,533 US6804651B2 (en) 2001-03-20 2002-03-19 Method and device for determining a measure of quality of an audio signal
DE50202226T DE50202226D1 (de) 2001-03-20 2002-03-19 Verfahren und vorrichtung zur bestimmung eines qualitätsmasses eines audiosignals
PCT/CH2002/000164 WO2002075725A1 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio

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Application Number Priority Date Filing Date Title
EP01810285A EP1244094A1 (fr) 2001-03-20 2001-03-20 Procédé et dispositif de détermination de la qualité d'un signal audio

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EP1244094A1 true EP1244094A1 (fr) 2002-09-25

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EP02703438.8A Expired - Lifetime EP1386307B2 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio

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US (1) US6804651B2 (fr)
EP (2) EP1244094A1 (fr)
AT (1) ATE289109T1 (fr)
DE (1) DE50202226D1 (fr)
WO (1) WO2002075725A1 (fr)

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WO2002075725A1 (fr) 2002-09-26
EP1386307A1 (fr) 2004-02-04
ATE289109T1 (de) 2005-02-15
US20020191798A1 (en) 2002-12-19
DE50202226D1 (de) 2005-03-17
EP1386307B2 (fr) 2013-04-17
US6804651B2 (en) 2004-10-12

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