Abstract
Single channel speech enhancement is typically referred to the methods in which a filter is applied to the noisy speech to recover enhanced speech signal. In these methods, noise reduction causes speech distortion. So, it is a key concern to control the tradeoff between noise reduction and speech distortion in designing speech enhancement algorithms. This paper deals with the frequency domain single channel speech enhancement performed via short time furrier transform (STFT). Conventional frequency domain methods treat the STFT coefficients independently ignoring neighboring correlations. In this paper, we take into account neighboring correlations and derive a minimum variance distortionless response filter in the frequency domain. Experimental results show merits of our proposed method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Benesty, J., & Chen, J. (2011). Optimal time-domain noise reduction filters—A theoretical study. Springer briefs in electrical and computer engineering. New York: Springer.
Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Noise reduction in speech processing. New York: Springer.
Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech and Signal Processing, 27, 113–120.
Chen, B., & Loizou, C. (2007). A Laplacian-based MMSE estimator for speech enhancement. Speech Communication, 49, 134–143.
Chen, I., & Benesty, J. (2012). Single-channel noise reduction in the STFT domain based on the bifrequency spectrum. In Proceeding of IEEE international conference on acoustics, speech and signal processing, pp. 97–100.
Cohen, I. (2003). Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging. IEEE Transactions on Speech and Audio Processing, 11, 466–475.
Ephraim, Y., & Malah, D. (1984). Speech enhancement using a minimum mean square error short time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing, 32, 1109–1130.
Hermus, K., Wambacq, P., & Hamme, H. V. (2007). A review of signal subspace speech enhancement and its application to noise robust speech recognition. EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1155/2007/45821.
Hu, Y., & Loizou, P. C. (2006). Evaluation of objective measures for speech enhancement. In Proceedings of interspeech, pp. 1447–1450.
Hu, Y., & Loizou, P. C. (2007). Subjective comparison and evaluation of speech enhancement algorithms. Speech Communication, 49, 588–601.
Huang, H., Zhao, L., Chen, J., & Benesty, J. (2014). A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction. Digital Signal Processing, 33, 169–179.
Huang, Y. A., & Benesty, J. (2012). A multi-frame approach to the frequency-domain single-channel noise reduction problem. IEEE Transactions on Audio, Speech, and Language Processing, 20, 1256–1269.
ITU. (2000). Perceptual evaluation of speech quality (PESQ), and objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs. In ITU-T recommendation 862.
Jensen, J. R., Benesty, J., Christensen, M. G., & Chen, J. (2013). A class of optimal rectangular filtering matrices for single-channel signal enhancement in the time domain. IEEE Transactions on Audio, Speech, and Language Processing, 21, 2595–2606.
Loizou, P. C. (2007). Speech enhancement: Theory and practice. Boca Raton: CRC Press.
Lu, Y., & Loizou, P. C. (2008). A geometric approach to spectral subtraction. Speech Communication, 50, 453–466.
Pearce, D., & Hirsch, H. G. (2000). The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In Proceeding of international conference on spoken language processing.
Saadoune, A., Amrouche, A., & Selouani, S. A. (2014). Perceptual subspace speech enhancement using variance of the reconstruction error. Digital Signal Processing, 24, 187–196.
Trawicki, M. B., & Johnson, M. T. (2014). Speech enhancement using Bayesian estimators of the perceptually-motivated short-time spectral amplitude (STSA) with Chi speech priors. Speech Communication, 57, 101–113.
Upadhyay, N., & Karmakar, A. (2015). Speech enhancement using spectral subtraction-type algorithms: A comparison and simulation study. Procedia Computer Science, 54, 574–584.
Varga, A., & Steeneken, H. J. M. (1993). NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems. Speech Communication, 12, 247–251.
Vincent, E., Fevotte, C., & Gribonval, R. (2006). Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech and Language Processing, 14, 1462–1469.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kammi, S. Single channel speech enhancement using an MVDR filter in the frequency domain. Int J Speech Technol 22, 383–389 (2019). https://doi.org/10.1007/s10772-019-09613-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-019-09613-w