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ISSN: 2278 – 1323
                                        International Journal of Advanced Research in Computer Engineering & Technology
                                                                                             Volume 1, Issue 5, July 2012



        Determination of Noise Levels in Using AMS
         Features of Noisy Speech Signal and Their
                        Comparison
                                                 Mrs. Anusha Krishna Prakash,
                                                                      makes it more difficult for them to escape poverty by
Abstract— Great difficulty in recognizing speech is under a            hindering progress in school or in the workplace and by
noisy background. The signal to noise ratio plays a very               isolating them socially. For countries, the cost of special
important role in speech recognition techniques. The signal to         education and lost employment due to hearing impairment
noise ratio is the ratio of the signal estimate to noise estimate in   can burden the economy. The hearing difficulties that normal
a particular speech signal. It has many applications in speech
                                                                       hearing listeners facing in the world are aging factor, if they
recognition techniques and speech pattern classification
schemes. This project helps in finding the signal to noise ratio       get any hearing ailments they lose their hearing efficiency.
estimates between the noises and reconstructed noise free              Some of the hearing difficulties create problems such that the
speech segment                                                         listeners lose their ability to recognize the spoken words
                                                                       underlying the speech. Even at noisy atmospheres even
          In this phase the amplitude modulation spectrogram           normal persons fail to recognize the speech. In this phase the
features are extracted from the noisy speech segment and they          amplitude modulation spectrogram features are determined
are classified into noise dominated and signal dominated               for the speech signal and the SNR values between the noisy
features using baye’s classifier. The classified features are          signal and the reconstructed signal.
reconstructed using overlap and add method. The signal to
noise ratio is calculated between the reconstructed speech
segment and the noises that the speech segment is corrupted.

Index Terms— Amplitude modulation spectrogram, signal to
noise ratio, baye’s classifier and overlap and add method.




                       I. INTRODUCTION
          Hearing impairment refers to both complete and
partial loss of the ability to hear. There are two types of
hearing impairment, according to which part of the ear is
affected. Conductive hearing impairment is a problem in the
outer or middle ear. It is often medically or surgically
treatable. A common example is chronic middle ear                              Fig 1. Block Diagram of Feature Extraction
infection. Sensorineural hearing impairment is a problem
with the inner ear, or, occasionally with the hearing nerve. It
is usually permanent and requires rehabilitation such as the                            II. FEATURE EXTRACTION
use of a hearing aid. Hearing impairment can impose a heavy                       The block diagram explains the procedure of feature
social and economic burden on individuals, families,                   extraction. At first a noisy speech signal and a clean speech
communities and countries.                                             signal are taken and they are passed through the band pass
          Hearing impairment in children may delay
                                                                       filter to get 25 channel bands according to Mel frequency. To
development of language and cognitive skills, which may
hinder progress in school. In adults, hearing impairment               these channel bands fast Fourier transforms are applied to get
often makes it difficult to obtain, perform, and keep jobs.            the envelopes of each band and to get the DC components of
Hearing impaired children and adults are often stigmatized             noisy speech signal and clean speech signal. Each envelope is
and socially isolated. The poor suffer more from hearing               decimated by the factor 3 and they are segmented. As a result
impairment because they cannot afford the preventive and               of segmentation, we get the 128 samples and 64 overlapping
routine care to avoid hearing loss nor the hearing aids to             samples after the application of Hann window to these
make the disability manageable. Hearing impairment also
                                                                       samples and again applying the fast Fourier transforms we
  Manuscript received June 25, 2012.                                   get modulation spectrum amplitudes which are the AMS
Mrs. Anusha Krishna Prakash, Department of Bio- Medical                features. Along with these AMS features we determine the
Instrumentation Engineering, Avinashilingam Institute for              delta feature values and we combine with those feature
Home Science and Higher Education For Women University,                vectors to get the feature vectors which are 45 in number. By
Coimbatore, India,                                                     classifying these features using the Bayesian classifiers and

                                                                                                                                   75
                                                 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323
                                     International Journal of Advanced Research in Computer Engineering & Technology
                                                                                          Volume 1, Issue 5, July 2012

the SNR is estimated between the two sets of speech
segments i.e., SNR is estimated between the clean speech
signal and noisy speech segment and the SNR is calculated
between the clean speech segment and reconstructed speech                                                                - (1)
segment.

                                                                   Then the P(λ1|Ay (τ,k)) is calculated in a similar way. These
         Band pass filter is generally used to allow the
                                                                 probabilistic features are reconstructed using overlap and add
signals falling within some band of frequencies. This filter
                                                                 method.
will segregate the signal into 25 channel bands according to
mel frequency spacing. Each band is full wave rectified to
change it to DC components such that the DC components                 IV. OVERLAP AND ADD METHOD OF RECONSTRUCTION
containing the noise can be retained or eliminated. This FFT
                                                                          Overlap and add method of reconstruction of the
is used to construct the envelope in each band. This is then
                                                                 speech signal is a common method which is also used for
decimated by the factor 3. This decimation is to reduce the
                                                                 reconstruction. It has many applications in reconstruction of
bandwidth and to do reliable filtering in modulation domain.
                                                                 various signal. It mainly involves the time scale modification
While applying the fast Fourier transforms we use various
                                                                 of the signal. It is a common time domain technique. This
types windowing function here are some of the windows
                                                                 method first collects the similar segments of the speech signal
given below. The windowing technique used in this project is
                                                                 which is devoid of noise i.e., it collects the DC components
Hanning window. The Hann and Hamming windows, both of
                                                                 of speech signal where the signal dominated components are
which are in the family known as "raised cosine" or
                                                                 taken. These frames are taken as overlapping segment which
"generalized Hamming" windows, are respectively named
                                                                 has 0.4 millisecond duration. These overlapping frames are
after Julius von Hann and Richard Hamming. The term
                                                                 added together to form the reconstructed noise free speech
"Hanning window" is sometimes used to refer to the Hann
                                                                 signal.
window. The reason for using this window is that when
comparing to other windowing techniques it has better
frequency resolution and spectral leakage and amplitude          V. SNR ESTIMATION BETWEEN CLEAN SPEECH SEGMENT AND NOISY
accuracy. Fast Fourier transforms are applied to each                               SPEECH SEGMENT
segment after padding it with the zeros. The main use of the               Signal-to-noise ratio is defined as the power ratio
fast Fourier transform is that it can calculate the modulation   between a signal and the background noise. The signal to
spectrum of each channel. To each band 15 triangular shaped      noise ratio is an important feature in determining the quality
windows are multiplied to get modulation spectral                of the audio data. This is very important in speech
amplitudes. These amplitudes are nothing but the amplitude       recognition process since the SNR values strongly influence
modulation spectrogram (AMS) features. These features            the speech recognition performance. In most of the
have a frequency resolution of 15.6Hz.                           applications the SNR values are not easily determined since
.                                                                the noise estimate is not known. This introduces the short
                                                                 term analysis of speech signal given the prior knowledge of
        III. CLASSIFICATION AND RECONSTRUCTION
                                                                 speech data to characterize the noise and speech data.
  A. Bayesian classification
   Classification of amplitude modulation spectrogram
                                                                          SNR= 10log10(Signal Energy/ Noise Energy) – (2)
  (AMS) features is done using Bayesian classifiers.
  Bayesian classifier involves classification of the features
                                                                          Here the signal to noise ratio is calculated between
  into either noise dominated features or signal dominated
                                                                 the noise signal and reconstructed signal. This is calculated
  features. This is done by comparing the probabilities
                                                                 using the formula,
  P(λ0|Ay (τ,k)) and P(λ1 | Ay (τ,k)) .
                                                                          SNR = log10 (Fo – F)2/ log10 Fo2        - (3)
   Where P(λ0|Ay (τ,k)) is the noise dominated amplitude
                                                                          Where Fo – reconstructed speech segment and F –
 modulation spectrogram features. These features are not
                                                                 noise dominated speech segment. Here each speech signal is
 considered for reconstruction since they are the features of
                                                                 of 2 seconds duration. Five types of speech segments are
 interest.
                                                                 used. They are bigtips, butter, draw, peaches and scholar
   P(λ1|Ay (τ,k)) is the signal dominated amplitude
                                                                 speech segments. Each speech segment is corrupted with six
  modulation       spectrogram features and these are features
                                                                 different types of noises. The noises used for corrupting the
  of interest and they are used for the reconstruction.
                                                                 speech segments are, burst_r1, f16_r1, factory_r1, pink_r1,
  The probability P(λ0|Ay (τ,k)) is found by using the
                                                                 Volvo_r1 and white_r1.
 equation




                                                                                                                             76
ISSN: 2278 – 1323
                                     International Journal of Advanced Research in Computer Engineering & Technology
                                                                                          Volume 1, Issue 5, July 2012

         Here the signal to noise ratio is calculated between
the clean speech segment and reconstructed speech segment.
This is calculated using the formula,                                        PEACHES – SPEECH SEGMENT

                                                                       Noisy         SNR values         SNR values
        SNR = log10 (F0 – F1)2/ log10 F02    -(4)
Where Fo – reconstructed speech segment and F1 – original              speech        between the        between the
clean speech segment.                                                  signal        original and       original and
                                                                                     reconstructed      noisy signal
                  VI. RESULTS AND DISCUSSIONS                                        signal
                                                                       Burst-r1      1.4240             21.4579
The tables given below portray the two sets of signal to noise
                                                                       F16_r1        1.4107             8.8095
ratio estimates between the clear speech segment and
reconstructed speech and between noisy speech and                      Factory_r     1.7055             9.7530
reconstructed speech segment for the corresponding speech              1
segments.
                                                                       Pink_r1       1.1729             8.4101
             BIGTIPS- SPEECH SEGMENT
                                                                       Volvo_r1      1.2274             13.0101
                     SNR values         SNR values
                                                                       White_r1      1.2735             7.8040
     Noisy           between the        between the
                                                                    Table – 3 Signal to noise ratio for Peaches type speech
     speech          original and       original and
                                                                                           segment
     signal          reconstructed      noisy signal                          DRAW – SPEECH SEGMENT
                     signal
                                                                      Noisy         SNR values between        SNR values
     Burst_r1        0.8790             20.7209                       speech        the original and          between the
     F16_r1          1.9107             5.3630                        signal        reconstructed signal      original and
                                                                                                              noisy signal
     Factory_r1      2.0050             5.9266
                                                                      Burst-r1      0.3638                    21.6511
     Pink_r1         1.1534             5.0261
                                                                      F16_r1        0.4792                    9.0468
     Volvo_r1        2.4397             8.8841                        Factory_r1    0.4295                    10.0269
     White_r1        1.1603             5.0470                        Pink_r1       0.4656                    8.4262
   Table – 1 Signal to noise ratio for Bigtips type speech            Volvo_r1      0.3452                    13.2181

              BUTTER – SPEECH SEGMENT                                White_r1       0.2285                   8.7704
                                                                 Table – 4 Signal to noise ratio for Draw type speech segment
                    SNR values       SNR values
     Noisy          between the      between the
                                                                                       VII. FUTURE SCOPE
     speech         original and     original and noisy
                                                                          From these signal to noise estimates it is evident that
     signal         reconstructed    signal                      noise ratio has reduced when comparing the SNRs between
                    signal                                       the original clean segment and noisy speech segment and
     Burst-r1       3.1422           26.0417                     SNRs between the original clean speech segment and
                                                                 reconstructed speech segment. Using this method the speech
     F16_r1         3.2954           6.5271                      segments are segregated into similar patterns. These patterns
     Factory_r      3.2829           7.0432                      are classified according to the degree of similarity. This
     1                                                           method is also used in various speech recognition techniques
                                                                 where the noise estimates are finalized with the degree of the
     Pink_r1        3.5784           5.8888                      signal to noise ratio. Drawback of this project is continuous
     Volvo_r1       3.2512           10.3689                     training with various noisy speech signal and their
                                                                 corresponding speech segments are needed.
     White_r1       2.2031           6.4620
Table – 2 Signal to noise ratio for Butter type speech segment



                                                                                                                              77
                                               All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323
                                             International Journal of Advanced Research in Computer Engineering & Technology
                                                                                                  Volume 1, Issue 5, July 2012




                             REFERENCES
[1]   Gibak Kim, Yang Lu, Yi Hu, and Philipos C. Loizou ‘An algorithm
      that improves the speech intelligibility for normal human listeners’
      2009 Acoustical Society of America. DOI: 10.1121/1.3184603 PACS
      numbers: 43.72.Ar, 43.72.Dv MSS Pages: 1486–1494
[2]   Jürgen Tchorz and Birger Kollmeier ’SNR estimation based on
      amplitude modulation analysis with applications to noise suppression’
      IEEE transactions on speech and audio processing, vol. 11, no. 3, may
      2003.
[3]   Jorn Anemuller, Denny Schmidt, Jorg-Hendrik Bach ‘Detection of
      Speech Embedded in Real Acoustic Background Based on Amplitude
      Modulation Spectrogram Features’ Accepted after peer review of full
      paper Copyright © 2008 ISCA pages 2582-2585.
[4]   Jorg-Hendrik    Bach,     Birger     Kollmeier,    Jorn    Anemuller
      ‘modulation-based detection of speech in real background noise:
      Generalization       to        novel       background        classes’
      978-1-4244-4296-6/10/$25.00 ©2010 IEEE pages(41 – 44).
[5]   Neri merhav, member IEEE and Yariv . E. Phraim, Senior member
      IEEE ’ A Bayesian classification approach with application to speech
      recognition’ IEEE Transaction on Signal Processing Vol. 39, No. 10,
      Oct 1991 Pages(2157 – 2166).
[6]   Peter H. W. Wong, Oscar C. Au, Justy W.C. Wong, William H. B. Lau
      ‘On improving the intelligibility of synchronized overlap and add at
      low TSM factor’.
[7]   Xin Ma, Weidong Zhou ‘AMS based spectrum subtraction algorithm
      with confidence interval test’ Yi Peng, Xiaohong Weng (Eds.):
      APCMBE 2008, IFMBE Proceedings 19, pp. 389–391, 2008.




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  • 1. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Determination of Noise Levels in Using AMS Features of Noisy Speech Signal and Their Comparison Mrs. Anusha Krishna Prakash,  makes it more difficult for them to escape poverty by Abstract— Great difficulty in recognizing speech is under a hindering progress in school or in the workplace and by noisy background. The signal to noise ratio plays a very isolating them socially. For countries, the cost of special important role in speech recognition techniques. The signal to education and lost employment due to hearing impairment noise ratio is the ratio of the signal estimate to noise estimate in can burden the economy. The hearing difficulties that normal a particular speech signal. It has many applications in speech hearing listeners facing in the world are aging factor, if they recognition techniques and speech pattern classification schemes. This project helps in finding the signal to noise ratio get any hearing ailments they lose their hearing efficiency. estimates between the noises and reconstructed noise free Some of the hearing difficulties create problems such that the speech segment listeners lose their ability to recognize the spoken words underlying the speech. Even at noisy atmospheres even In this phase the amplitude modulation spectrogram normal persons fail to recognize the speech. In this phase the features are extracted from the noisy speech segment and they amplitude modulation spectrogram features are determined are classified into noise dominated and signal dominated for the speech signal and the SNR values between the noisy features using baye’s classifier. The classified features are signal and the reconstructed signal. reconstructed using overlap and add method. The signal to noise ratio is calculated between the reconstructed speech segment and the noises that the speech segment is corrupted. Index Terms— Amplitude modulation spectrogram, signal to noise ratio, baye’s classifier and overlap and add method. I. INTRODUCTION Hearing impairment refers to both complete and partial loss of the ability to hear. There are two types of hearing impairment, according to which part of the ear is affected. Conductive hearing impairment is a problem in the outer or middle ear. It is often medically or surgically treatable. A common example is chronic middle ear Fig 1. Block Diagram of Feature Extraction infection. Sensorineural hearing impairment is a problem with the inner ear, or, occasionally with the hearing nerve. It is usually permanent and requires rehabilitation such as the II. FEATURE EXTRACTION use of a hearing aid. Hearing impairment can impose a heavy The block diagram explains the procedure of feature social and economic burden on individuals, families, extraction. At first a noisy speech signal and a clean speech communities and countries. signal are taken and they are passed through the band pass Hearing impairment in children may delay filter to get 25 channel bands according to Mel frequency. To development of language and cognitive skills, which may hinder progress in school. In adults, hearing impairment these channel bands fast Fourier transforms are applied to get often makes it difficult to obtain, perform, and keep jobs. the envelopes of each band and to get the DC components of Hearing impaired children and adults are often stigmatized noisy speech signal and clean speech signal. Each envelope is and socially isolated. The poor suffer more from hearing decimated by the factor 3 and they are segmented. As a result impairment because they cannot afford the preventive and of segmentation, we get the 128 samples and 64 overlapping routine care to avoid hearing loss nor the hearing aids to samples after the application of Hann window to these make the disability manageable. Hearing impairment also samples and again applying the fast Fourier transforms we Manuscript received June 25, 2012. get modulation spectrum amplitudes which are the AMS Mrs. Anusha Krishna Prakash, Department of Bio- Medical features. Along with these AMS features we determine the Instrumentation Engineering, Avinashilingam Institute for delta feature values and we combine with those feature Home Science and Higher Education For Women University, vectors to get the feature vectors which are 45 in number. By Coimbatore, India, classifying these features using the Bayesian classifiers and 75 All Rights Reserved © 2012 IJARCET
  • 2. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 the SNR is estimated between the two sets of speech segments i.e., SNR is estimated between the clean speech signal and noisy speech segment and the SNR is calculated between the clean speech segment and reconstructed speech - (1) segment. Then the P(λ1|Ay (τ,k)) is calculated in a similar way. These Band pass filter is generally used to allow the probabilistic features are reconstructed using overlap and add signals falling within some band of frequencies. This filter method. will segregate the signal into 25 channel bands according to mel frequency spacing. Each band is full wave rectified to change it to DC components such that the DC components IV. OVERLAP AND ADD METHOD OF RECONSTRUCTION containing the noise can be retained or eliminated. This FFT Overlap and add method of reconstruction of the is used to construct the envelope in each band. This is then speech signal is a common method which is also used for decimated by the factor 3. This decimation is to reduce the reconstruction. It has many applications in reconstruction of bandwidth and to do reliable filtering in modulation domain. various signal. It mainly involves the time scale modification While applying the fast Fourier transforms we use various of the signal. It is a common time domain technique. This types windowing function here are some of the windows method first collects the similar segments of the speech signal given below. The windowing technique used in this project is which is devoid of noise i.e., it collects the DC components Hanning window. The Hann and Hamming windows, both of of speech signal where the signal dominated components are which are in the family known as "raised cosine" or taken. These frames are taken as overlapping segment which "generalized Hamming" windows, are respectively named has 0.4 millisecond duration. These overlapping frames are after Julius von Hann and Richard Hamming. The term added together to form the reconstructed noise free speech "Hanning window" is sometimes used to refer to the Hann signal. window. The reason for using this window is that when comparing to other windowing techniques it has better frequency resolution and spectral leakage and amplitude V. SNR ESTIMATION BETWEEN CLEAN SPEECH SEGMENT AND NOISY accuracy. Fast Fourier transforms are applied to each SPEECH SEGMENT segment after padding it with the zeros. The main use of the Signal-to-noise ratio is defined as the power ratio fast Fourier transform is that it can calculate the modulation between a signal and the background noise. The signal to spectrum of each channel. To each band 15 triangular shaped noise ratio is an important feature in determining the quality windows are multiplied to get modulation spectral of the audio data. This is very important in speech amplitudes. These amplitudes are nothing but the amplitude recognition process since the SNR values strongly influence modulation spectrogram (AMS) features. These features the speech recognition performance. In most of the have a frequency resolution of 15.6Hz. applications the SNR values are not easily determined since . the noise estimate is not known. This introduces the short term analysis of speech signal given the prior knowledge of III. CLASSIFICATION AND RECONSTRUCTION speech data to characterize the noise and speech data. A. Bayesian classification Classification of amplitude modulation spectrogram SNR= 10log10(Signal Energy/ Noise Energy) – (2) (AMS) features is done using Bayesian classifiers. Bayesian classifier involves classification of the features Here the signal to noise ratio is calculated between into either noise dominated features or signal dominated the noise signal and reconstructed signal. This is calculated features. This is done by comparing the probabilities using the formula, P(λ0|Ay (τ,k)) and P(λ1 | Ay (τ,k)) . SNR = log10 (Fo – F)2/ log10 Fo2 - (3) Where P(λ0|Ay (τ,k)) is the noise dominated amplitude Where Fo – reconstructed speech segment and F – modulation spectrogram features. These features are not noise dominated speech segment. Here each speech signal is considered for reconstruction since they are the features of of 2 seconds duration. Five types of speech segments are interest. used. They are bigtips, butter, draw, peaches and scholar P(λ1|Ay (τ,k)) is the signal dominated amplitude speech segments. Each speech segment is corrupted with six modulation spectrogram features and these are features different types of noises. The noises used for corrupting the of interest and they are used for the reconstruction. speech segments are, burst_r1, f16_r1, factory_r1, pink_r1, The probability P(λ0|Ay (τ,k)) is found by using the Volvo_r1 and white_r1. equation 76
  • 3. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Here the signal to noise ratio is calculated between the clean speech segment and reconstructed speech segment. This is calculated using the formula, PEACHES – SPEECH SEGMENT Noisy SNR values SNR values SNR = log10 (F0 – F1)2/ log10 F02 -(4) Where Fo – reconstructed speech segment and F1 – original speech between the between the clean speech segment. signal original and original and reconstructed noisy signal VI. RESULTS AND DISCUSSIONS signal Burst-r1 1.4240 21.4579 The tables given below portray the two sets of signal to noise F16_r1 1.4107 8.8095 ratio estimates between the clear speech segment and reconstructed speech and between noisy speech and Factory_r 1.7055 9.7530 reconstructed speech segment for the corresponding speech 1 segments. Pink_r1 1.1729 8.4101 BIGTIPS- SPEECH SEGMENT Volvo_r1 1.2274 13.0101 SNR values SNR values White_r1 1.2735 7.8040 Noisy between the between the Table – 3 Signal to noise ratio for Peaches type speech speech original and original and segment signal reconstructed noisy signal DRAW – SPEECH SEGMENT signal Noisy SNR values between SNR values Burst_r1 0.8790 20.7209 speech the original and between the F16_r1 1.9107 5.3630 signal reconstructed signal original and noisy signal Factory_r1 2.0050 5.9266 Burst-r1 0.3638 21.6511 Pink_r1 1.1534 5.0261 F16_r1 0.4792 9.0468 Volvo_r1 2.4397 8.8841 Factory_r1 0.4295 10.0269 White_r1 1.1603 5.0470 Pink_r1 0.4656 8.4262 Table – 1 Signal to noise ratio for Bigtips type speech Volvo_r1 0.3452 13.2181 BUTTER – SPEECH SEGMENT White_r1 0.2285 8.7704 Table – 4 Signal to noise ratio for Draw type speech segment SNR values SNR values Noisy between the between the VII. FUTURE SCOPE speech original and original and noisy From these signal to noise estimates it is evident that signal reconstructed signal noise ratio has reduced when comparing the SNRs between signal the original clean segment and noisy speech segment and Burst-r1 3.1422 26.0417 SNRs between the original clean speech segment and reconstructed speech segment. Using this method the speech F16_r1 3.2954 6.5271 segments are segregated into similar patterns. These patterns Factory_r 3.2829 7.0432 are classified according to the degree of similarity. This 1 method is also used in various speech recognition techniques where the noise estimates are finalized with the degree of the Pink_r1 3.5784 5.8888 signal to noise ratio. Drawback of this project is continuous Volvo_r1 3.2512 10.3689 training with various noisy speech signal and their corresponding speech segments are needed. White_r1 2.2031 6.4620 Table – 2 Signal to noise ratio for Butter type speech segment 77 All Rights Reserved © 2012 IJARCET
  • 4. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 REFERENCES [1] Gibak Kim, Yang Lu, Yi Hu, and Philipos C. Loizou ‘An algorithm that improves the speech intelligibility for normal human listeners’ 2009 Acoustical Society of America. DOI: 10.1121/1.3184603 PACS numbers: 43.72.Ar, 43.72.Dv MSS Pages: 1486–1494 [2] Jürgen Tchorz and Birger Kollmeier ’SNR estimation based on amplitude modulation analysis with applications to noise suppression’ IEEE transactions on speech and audio processing, vol. 11, no. 3, may 2003. [3] Jorn Anemuller, Denny Schmidt, Jorg-Hendrik Bach ‘Detection of Speech Embedded in Real Acoustic Background Based on Amplitude Modulation Spectrogram Features’ Accepted after peer review of full paper Copyright © 2008 ISCA pages 2582-2585. [4] Jorg-Hendrik Bach, Birger Kollmeier, Jorn Anemuller ‘modulation-based detection of speech in real background noise: Generalization to novel background classes’ 978-1-4244-4296-6/10/$25.00 ©2010 IEEE pages(41 – 44). [5] Neri merhav, member IEEE and Yariv . E. Phraim, Senior member IEEE ’ A Bayesian classification approach with application to speech recognition’ IEEE Transaction on Signal Processing Vol. 39, No. 10, Oct 1991 Pages(2157 – 2166). [6] Peter H. W. Wong, Oscar C. Au, Justy W.C. Wong, William H. B. Lau ‘On improving the intelligibility of synchronized overlap and add at low TSM factor’. [7] Xin Ma, Weidong Zhou ‘AMS based spectrum subtraction algorithm with confidence interval test’ Yi Peng, Xiaohong Weng (Eds.): APCMBE 2008, IFMBE Proceedings 19, pp. 389–391, 2008. 78