TY - UNPB
T1 - DCTNet and PCANet for acoustic signal feature extraction
AU - Xian, Yin
AU - Thompson, Andrew
AU - Sun, Xiaobai
AU - Nowacek, Douglas
AU - Nolte, Loren
PY - 2016/4/28
Y1 - 2016/4/28
N2 - We introduce the use of DCTNet, an efficient approximation and alternative to PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of the local sample covariance matrix (PCA) are used as filterbanks for convolution and feature extraction. When the eigenfunctions are well approximated by the Discrete Cosine Transform (DCT) functions, each layer of of PCANet and DCTNet is essentially a time-frequency representation. We relate DCTNet to spectral feature representation methods, such as the the short time Fourier transform (STFT), spectrogram and linear frequency spectral coefficients (LFSC). Experimental results on whale vocalization data show that DCTNet improves classification rate, demonstrating DCTNet's applicability to signal processing problems such as underwater acoustics.
AB - We introduce the use of DCTNet, an efficient approximation and alternative to PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of the local sample covariance matrix (PCA) are used as filterbanks for convolution and feature extraction. When the eigenfunctions are well approximated by the Discrete Cosine Transform (DCT) functions, each layer of of PCANet and DCTNet is essentially a time-frequency representation. We relate DCTNet to spectral feature representation methods, such as the the short time Fourier transform (STFT), spectrogram and linear frequency spectral coefficients (LFSC). Experimental results on whale vocalization data show that DCTNet improves classification rate, demonstrating DCTNet's applicability to signal processing problems such as underwater acoustics.
KW - Convolutional networ
KW - PCA
KW - DCT
KW - filterbanks
KW - acoustic perception
KW - spectral clustering
KW - whale vocalizations
UR - https://arxiv.org/abs/1605.01755
U2 - 10.48550/ARXIV.1605.01755
DO - 10.48550/ARXIV.1605.01755
M3 - Preprint
T3 - arXiv
BT - DCTNet and PCANet for acoustic signal feature extraction
PB - arXiv
ER -