An evaluation of noise power spectral density estimation algorithms in adverse acoustic environments

Noise power spectral density estimation is an important component of speech enhancement systems due to its considerable effect on the quality and the intelligibility of the enhanced speech. Recently, many new algorithms have been proposed and significant progress in noise tracking has been made. In this paper, we present an evaluation framework for measuring the performance of some recently proposed and some well-known noise power spectral density estimators and compare their performance in adverse acoustic environments. In this investigation we do not only consider the performance in the mean of a spectral distance measure but also evaluate the variance of the estimators as the latter is related to undesirable fluctuations also known as musical noise. By providing a variety of different non-stationary noises, the robustness of noise estimators in adverse environments is examined.

[1]  Yi Hu,et al.  Subjective Comparison of Speech Enhancement Algorithms , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[2]  Jesper Jensen,et al.  MMSE based noise PSD tracking with low complexity , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Justinian P. Rosca,et al.  Speech Noise Estimation using Enhanced Minima Controlled Recursive Averaging , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Rongshan Yu A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Ephraim Speech enhancement using a minimum mean square error short-time spectral amplitude estimator , 1984 .

[6]  Jesper Jensen,et al.  Noise Tracking Using DFT Domain Subspace Decompositions , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Rainer Martin,et al.  Noise power spectral density estimation based on optimal smoothing and minimum statistics , 2001, IEEE Trans. Speech Audio Process..

[8]  Rainer Martin,et al.  Spectral Subtraction Based on Minimum Statistics , 2001 .

[9]  Israel Cohen,et al.  Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging , 2003, IEEE Trans. Speech Audio Process..

[10]  I. Cohen,et al.  Noise estimation by minima controlled recursive averaging for robust speech enhancement , 2002, IEEE Signal Processing Letters.

[11]  Jonathan G. Fiscus,et al.  Darpa Timit Acoustic-Phonetic Continuous Speech Corpus CD-ROM {TIMIT} | NIST , 1993 .

[12]  Jong-Mo Kum,et al.  Speech enhancement based on minima controlled recursive averaging incorporating conditional maximum a posteriori criterion , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Rainer Martin,et al.  A novel a priori SNR estimation approach based on selective cepstro-temporal smoothing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.