Speech/Music Classification Using Features From Spectral Peaks

Abstract

Spectrograms of speech and music contain distinct striation patterns. Traditional features represent various properties of the audio signal but do not necessarily capture such patterns. This work proposes to model such spectrogram patterns using a novel Spectral Peak Tracking (SPT) approach. Two novel time-frequency features for speech vs. music classification are proposed. The proposed features are extracted in two stages. First, SPT is performed to track a preset number of highest amplitude spectral peaks in an audio interval. In the second stage, the location and amplitudes of these peak traces are used to compute the proposed feature sets. The first feature involves the computation of mean and standard deviation of peak traces. The second feature is obtained as averaged component posterior probability vectors of Gaussian mixture models learned on the peak traces. Speech vs. music classification is performed by training various binary classifiers on these proposed features. Three standard datasets are used to evaluate the efficiency of the proposed features for speech/music classification. The proposed features are benchmarked against five baseline approaches. Finally, the best-proposed feature is combined with two contemporary deep-learning based features to show that such combinations can lead to more robust speech vs. music classification systems.