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CH. SR. Raj, and T.. V.. Sreenivas, "Compressive Sensing for Music Signals: Comparison of Ttransforms with Coherent Dictionaries," Paper 4-3, (2011 July.). doi: CH. SR. Raj, and T.. V.. Sreenivas, "Compressive Sensing for Music Signals: Comparison of Ttransforms with Coherent Dictionaries," Paper 4-3, (2011 July.). doi:
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.

@article{raj2011compressive,
author={raj, ch. srikanth and sreenivas, t. v.},
journal={journal of the audio engineering society},
title={compressive sensing for music signals: comparison of ttransforms with coherent dictionaries},
year={2011},
volume={},
number={},
pages={},
doi={},
month={july},} @article{raj2011compressive,
author={raj, ch. srikanth and sreenivas, t. v.},
journal={journal of the audio engineering society},
title={compressive sensing for music signals: comparison of ttransforms with coherent dictionaries},
year={2011},
volume={},
number={},
pages={},
doi={},
month={july},
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.},}

TY - paper
TI - Compressive Sensing for Music Signals: Comparison of Ttransforms with Coherent Dictionaries
SP - EP -
AU - Raj, Ch. Srikanth
AU - Sreenivas, T. V.
PY - 2011
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - July 2011 TY - paper
TI - Compressive Sensing for Music Signals: Comparison of Ttransforms with Coherent Dictionaries
SP - EP -
AU - Raj, Ch. Srikanth
AU - Sreenivas, T. V.
PY - 2011
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - July 2011
AB - 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.