Overlapped music segmentation using a new effective feature and random forests

Abstract

In the field of audio classification, audio signals may be broadly divided into three classes: speech, music and events. Most studies, however, neglect that real audio soundtracks can have any combination of these classes simultaneously. This can result in information loss, thus compromising the knowledge discovery. In this study, a novel feature, “Entrocy”, is proposed for the detection of music in both pure form and overlapping with the other audio classes. Entrocy is defined as the variation of the information (or entropy) in an audio segment over time. Segments, which contain music, were found to have lower Entrocy since there are fewer abrupt changes over time. We have also compared Entrocy with existing music detection features and the entrocy showing a good performance.