Acoustic Segmentation for Audio Browsers

Abstract

Online digital audio is a rapidly growing resource, which can be accessed in rich new ways not previously possible. For example, it is possible to listen to just those portions of a long discussion which involve a given subset of people, or to instantly skip ahead to the next speaker. Providing this capability to users, however, requires generation of necessary indices, as well as an interface which utilizes these indices to aid navigation.

We describe algorithms which generate indices from automatic acoustic segmentation. These algorithms use hidden Markov models to segment audio into segments corresponding to different speakers or acoustics classes (e.g. music). Unsupervised model initialization using agglomerative clustering is described, and shown to work as well in most cases as supervised initialization.

We also describe a user interface which displays the segmentation in the form of a timeline, which tracks for the different acoustic classes. The interface can be used for direct navigation through the audio.