Section: New Results
Online Methods for Audio Segmentation and Clustering
Audio segmentation is an essential problem in many audio signal processing tasks, which tries to segment an audio signal into homogeneous chunks. Rather than separately finding change points and computing similarities between segments, we focus on joint segmentation and clustering, using the framework of hidden Markov and semi-Markov models. We introduced a new incremental EM algorithm for hidden Markov models (HMMs) and showed that it compares favorably to existing online EM algorithms for HMMs. Early experimental results on musical note segmentation and environmental sound clustering are promising and will be pursued further in 2015.
This project was done in the context of Alberto Bietti's MS project  under co-supervision of Arshia Cont (MuTant) and Francis Bach (SIERRA).