Speech - nonspeech discrimination using the Information Bottleneck method and Spectro-Temporal Modulation Index

In this work, we adopt an information theoretic approach- the Information Bottleneck method- to extract the relevant spectro-temporal modulations for the task of speech / non-speech dis-crimination- non-speech events include music, noise and an-imal vocalizations. A compact representation (a “cluster pro-totype”) is built for each class consisting of the maximally in-formative features with respect to the classification task. We assess the similarity of a sound to each representative cluster using the spectro-temporal modulation index (STMI) adapted to handle the contribution of different frequency bands. A sim-ple threshold check is then used for discriminating speech from non-speech events. Conducted experiments have shown that the proposed method has low complexity and high accuracy of dis-crimination in low SNR conditions compared to recently pro-posed methods for the same task. Index Terms: audio classification, speech discrimination, au-ditory mode