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Conference Paper

Compressive Sensing for Music Signals: Comparison of Ttransforms with Coherent Dictionaries

Authors: Raj, Ch. Srikanth; Sreenivas, T. V.

AES Conference: 42nd International Conference: Semantic Audio · Paper 4-3 · July 2011

Abstract

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.

Details

Published in
AES Conference: 42nd International Conference: Semantic Audio
Paper number
4-3
Publication date
July 6, 2011
Session subject
Informed Source Separation
Affiliation
Indian Institute of Science, Bangalore, India (See document for exact affiliation information.)
Type
Conference Paper