Stacked auto-encoders for audio classification
Version 1.0.0 (3.02 KB) by
Arvind
We evaluates the performance of stacked auto-encoders for designing a speech/music classifier on S&S and GTZAN dataset
This work evaluates the performance of stacked auto-encoders based deep neural network for designing a speech/music classifier on S&S and GTZAN dataset using visual features. The hidden layers of the neural network are initially trained in unsupervised manner using auto-encoders and are stacked with the final softmax layer. Different experiments were conducted on time-frequency features derived from Spectrogram and Chromagram. Performances of the combination of stacked auto-encoder and softmax classifier was further compared with traditional classifiers and different deep learning techniques. Best classification accuracy of 93.05% and 94.73% is observed for fused features for S&S and GTZAN datasets respectively.
Cite As
Arvind (2024). Stacked auto-encoders for audio classification (https://www.mathworks.com/matlabcentral/fileexchange/132752-stacked-auto-encoders-for-audio-classification), MATLAB Central File Exchange. Retrieved .
Kumar, Arvind, et al. “Stacked Auto-Encoders Based Visual Features for Speech/Music Classification.” Expert Systems with Applications, vol. 208, Elsevier BV, Dec. 2022, p. 118041, doi:10.1016/j.eswa.2022.118041.
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