Abstract
In this paper, a novel sound denoising approach based on a statistical model of the power spectrogram of a sound signal is proposed by using an extended non-negative sparse coding (NNSC) algorithm for power spectra. This approach is self-adaptive to the statistic property of spectrograms of sounds. The basic idea for denoising is to exploit a shrinkage function to reduce noises in spectrogram patches. Experimental results show that our approach is indeed effective and efficient in spectrogram denoising. Compared with other denoising methods, the simulation results show that the NNSC shrinkage technique is indeed effective and efficient.
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© 2008 Springer-Verlag Berlin Heidelberg
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Shang, L., Cao, F., Zhang, J. (2008). A New Denoising Approach for Sound Signals Based on Non-negative Sparse Coding of Power Spectra. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_41
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DOI: https://doi.org/10.1007/978-3-540-87734-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
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