Abstract
We present a low complexity speech enhancement technique for real-life multi-source environments. Assuming that the speaker identity is known a priori, we present the idea of incorporating speaker model to enhance a target signal corrupted in non-stationary noise in a reverberant scenario. Based on experiments, this helps to improve the limited performance of noise-tracking based speech enhancement methods under unpredictable and non-stationary noise scenarios. Using pre-trained speaker model captures a constrained subspace for target speech and is capable to provide enhanced speech estimate by rejecting the non-stationary noise sources. Experimental results on Signal Separation Evaluation Campaign (SiSEC) showed that the proposed approach is successful in canceling the interference signal in the noisy input and providing an enhanced output signal.
The work of Pejman Mowlaee was funded by the European Commission within the Marie Curie ITN AUDIS, grant PITNGA-2008-214699. The work of Rahim Saeidi was funded by the European Community’s Seventh Framework Programme (FP7 2007-2013) under grant agreement no. 238803.
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References
Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. Audio, Speech, and Language Process. 32(6), 1109–1121 (1984)
Ephraim, Y., Malah, D.: Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing 33(2), 443–445 (1985)
Hendriks, R.C., Heusdens, R., Jensen, J.: MMSE based noise PSD tracking with low complexity. In: Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pp. 4266–4269 (2010)
Christensen, H., Barker, J., Ma, N., Green, P.: The CHiME corpus: a resource and a challenge for computational hearing in multisource environments. In: Proc. Interspeech, pp. 1918–1921 (2010)
Mowlaee, P.: New Stategies for Single-channel Speech Separation, Ph.D. thesis, Institut for Elektroniske Systemer, Aalborg Universitet (2010)
Mowlaee, P., Christensen, M., Jensen, S.: New results on single-channel speech separation using sinusoidal modeling. IEEE Trans. Audio, Speech, and Language Process. 19(5), 1265–1277 (2011)
Rangachari, S., Loizou, P.C.: A noise-estimation algorithm for highly non-stationary environments. Speech Communication 48(2), 220–231 (2006)
Cohen, I., Berdugo, B.: Speech enhancement for non-stationary noise environments. Signal Processing 81(11), 2403–2418 (2001)
Wang, D.: On ideal binary mask as the computational goal of auditory scene analysis. In: Speech Separation by Humans and Machines, pp. 181–197. Kluwer (2005)
Erkelens, J., Hendriks, R., Heusdens, R., Jensen, J.: Minimum mean-square error estimation of discrete Fourier coefficients with generalized gamma priors. IEEE Transactions on Audio, Speech, and Language Processing 15(6), 1741–1752 (2007)
Vincent, E., Gribonval, R., Fevotte, C.: Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech, and Language Processing 14(4), 1462–1469 (2006)
The third community-based Signal Separation Evaluation Campaign (SiSEC 2011), http://sisec.wiki.irisa.fr/tiki-index.php
Emiya, V., Vincent, E., Harlander, N., Hohmann, V.: Subjective and objective quality assessment of audio source separation. IEEE Transactions on Audio, Speech, and Language Processing (99), 1 (2011)
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Mowlaee, P., Saeidi, R., Martin, R. (2012). Model-Driven Speech Enhancement for Multisource Reverberant Environment (Signal Separation Evaluation Campaign (SiSEC) 2011). In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_56
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DOI: https://doi.org/10.1007/978-3-642-28551-6_56
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