Abstract
This paper describes a subband adaptive learning algorithm for enhancing microphone array speech signals degraded by a considerable amount of acoustic background noise. The subband multichannel adaptive learning algorithm is adopted to overcome the drawback of slow convergence as well as high computational complexity, which is associated with full band multichannel adaptive LMS algorithm. Simultaneously, oversampled Cosine-modulated filter banks instead of critical sampling filter banks are used to reduce the aliasing effects of subband itself. Simulations experiments show that in addition to fast convergence speed, the proposed microphone array speech enhancement method based on subband adaptive learning algorithm also exhibits a better noise reduction performance than well-known Generalized Sidelobe Canceller (GSC).
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, D., Yin, F. (2005). A Subband Adaptive Learning Algorithm for Microphone Array Based Speech Enhancement. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_97
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DOI: https://doi.org/10.1007/11427445_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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