Title :
An Information-Geometric Approach to Real-Time Audio Segmentation
Author :
Dessein, Arnaud ; Cont, Arshia
Author_Institution :
Music Representations Team, IRCAM, Paris, France
Abstract :
We present a generic approach to real-time audio segmentation in the framework of information geometry for exponential families. The proposed system detects changes by monitoring the information rate of the signals as they arrive in time. We also address shortcomings of traditional cumulative sum approaches to change detection, which assume known parameters before change. This is done by considering exact generalized likelihood ratio test statistics, with a complete estimation of the unknown parameters in the respective hypotheses. We derive an efficient sequential scheme to compute these statistics through convex duality. We finally provide results for speech segmentation in speakers, and polyphonic music segmentation in note slices.
Keywords :
audio streaming; geometry; music; parameter estimation; signal detection; speech processing; statistics; convex duality statistics; cumulative sum approach; efήcient sequential scheme; exact generalized likelihood ratio test statistics; information-geometric approach; note slice; parameter estimation; polyphonic music segmentation; real-time audio segmentation; signal detection; speaker segmentation; speech segmentation; Information geometry; Information rates; Multiple signal classification; Radio frequency; Real-time systems; Speech; Audio segmentation; change detection; information geometry; real-time system;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2247039