2,161 research outputs found
Adaptive DCTNet for Audio Signal Classification
In this paper, we investigate DCTNet for audio signal classification. Its
output feature is related to Cohen's class of time-frequency distributions. We
introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature
extraction. The A-DCTNet applies the idea of constant-Q transform, with its
center frequencies of filterbanks geometrically spaced. The A-DCTNet is
adaptive to different acoustic scales, and it can better capture low frequency
acoustic information that is sensitive to human audio perception than features
such as Mel-frequency spectral coefficients (MFSC). We use features extracted
by the A-DCTNet as input for classifiers. Experimental results show that the
A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art
performance in bird song classification rate, and improve artist identification
accuracy in music data. They demonstrate A-DCTNet's applicability to signal
processing problems.Comment: International Conference of Acoustic and Speech Signal Processing
(ICASSP). New Orleans, United States, March, 201
Chinese reverse M&As in the Netherlands : Chinese managers’ trust building practices
Author's accepted manuscript (postprint).acceptedVersio
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