SABIL SAJJAD - Academia.edu (original) (raw)
Address: Pointe-claire, Québec, Canada
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ABSTRACT We present in this paper a novel algorithm for single channel speech enhancement. It is ... more ABSTRACT We present in this paper a novel algorithm for single channel speech enhancement. It is based on a subspace approach in the Bark domain and an optimal subspace selection by the minimum description length (MDL) criterion. The processing in the Bark domain allows us to take into account in an optimal manner the masking properties of the human auditory system. The subspace selection provided by the MDL criterion overcomes the limitations encountered with other selection criteria, like the overestimation of the signal--plus--noise subspace or the need for empirical parameters. Together, the resulting MDLsubspace approach in the Bark domain provides maximum noise reduction while minimizing signal distortions. The performance of our algorithm is assessed in white and colored noise. It shows that our algorithm provides high performance for a large scale of input signal-to-noise ratio.
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various noise levels. This evaluation is used in order to find optimum method parameter values, thereby improving this algorithm to make it more appropriate for speech communication purposes. The system is implemented in MATLAB and validated by considering different performance measure and for different Signal to Noise Ratio Improvement (SNRI) and Spectral Distortion (SD). The SNRI and SD were calculated for different filter bank settings such as different number of subbands and for different decimation and interpolation ratios. The method provides efficient speech enhancement in terms of SNRI and SD performance measures.
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various...
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. This book focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various noise levels. This evaluation is used in order to find optimum method parameter values, thereby improving this algorithm to make it more appropriate for speech communication purposes. This...
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various...
ABSTRACT We present in this paper a novel algorithm for single channel speech enhancement. It is ... more ABSTRACT We present in this paper a novel algorithm for single channel speech enhancement. It is based on a subspace approach in the Bark domain and an optimal subspace selection by the minimum description length (MDL) criterion. The processing in the Bark domain allows us to take into account in an optimal manner the masking properties of the human auditory system. The subspace selection provided by the MDL criterion overcomes the limitations encountered with other selection criteria, like the overestimation of the signal--plus--noise subspace or the need for empirical parameters. Together, the resulting MDLsubspace approach in the Bark domain provides maximum noise reduction while minimizing signal distortions. The performance of our algorithm is assessed in white and colored noise. It shows that our algorithm provides high performance for a large scale of input signal-to-noise ratio.
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various noise levels. This evaluation is used in order to find optimum method parameter values, thereby improving this algorithm to make it more appropriate for speech communication purposes. The system is implemented in MATLAB and validated by considering different performance measure and for different Signal to Noise Ratio Improvement (SNRI) and Spectral Distortion (SD). The SNRI and SD were calculated for different filter bank settings such as different number of subbands and for different decimation and interpolation ratios. The method provides efficient speech enhancement in terms of SNRI and SD performance measures.
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various...
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. This book focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various noise levels. This evaluation is used in order to find optimum method parameter values, thereby improving this algorithm to make it more appropriate for speech communication purposes. This...
Speech is an elementary source of human interaction. The quality and intelligibility of speech si... more Speech is an elementary source of human interaction. The quality and intelligibility of speech signals during communication are generally degraded by the surrounding noise. Corrupted speech signals need therefore to be enhanced to improve quality and intelligibility. In the field of speech processing, much effort has been devoted to develop speech enhancement techniques in order to restore the speech signal by reducing the amount of disturbing noise. This thesis focuses on a single channel speech enhancement technique that performs noise reduction by spectral subtraction based on minimum statistics. Minimum statistics means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and, thus, circumvents the speech activity detection problem. The performance of the spectral subtraction method is evaluated using single channel speech data and for a wide range of noise types with various...