Stacked auto-encoders for audio classification

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.