Speech — Nonspeech discrimination based on speech-relevant spectrogram modulations

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. When we assess the performance of the classification system at various SNR conditions using F-measure, results are equally good to a recently proposed method based on the same features but having significantly greater complexity.