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Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Raj, Ch. Srikanth and Sreenivas, TV (2011) Compressive sensing for music signals: comparison of transforms with coherent dictionaries. In: 42nd International Conference: Semantic Audio, July 2011.

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Official URL: http://www.aes.org/e-lib/browse.cfm?elib=15946


Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Item Type: Conference Paper
Publisher: Audio Engineering Society
Additional Information: Copyright of this article belongs to Audio Engineering Society.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 02 Apr 2013 08:36
Last Modified: 02 Apr 2013 08:36
URI: http://eprints.iisc.ac.in/id/eprint/46144

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