Extraction of speech-relevant information from modulation spectrograms

Abstract

In this work, we adopt an information theoretic approach - the Information Bottleneck method - to extract the relevant modulation frequencies across both dimensions of a spectrogram, for speech / non-speech discrimination (music, animal vocalizations, environmental noises). A compact representation is built for each sound ensemble, consisting of the maximally informative features. We demonstrate the effectiveness of a simple thresholding classifier which is based on the similarity of a sound to each characteristic modulation spectrum. © Springer-Verlag Berlin Heidelberg 2007.