Title :
Sparse Representations in Audio and Music: From Coding to Source Separation
Author :
Plumbley, Mark D. ; Blumensath, Thomas ; Daudet, Laurent ; Gribonval, Rémi ; Davies, Mike E.
Author_Institution :
Sch. of Electron. Eng. & Comput. Sci., Queen Mary Univ. of London, London, UK
fDate :
6/1/2010 12:00:00 AM
Abstract :
Sparse representations have proved a powerful tool in the analysis and processing of audio signals and already lie at the heart of popular coding standards such as MP3 and Dolby AAC. In this paper we give an overview of a number of current and emerging applications of sparse representations in areas from audio coding, audio enhancement and music transcription to blind source separation solutions that can solve the ??cocktail party problem.?? In each case we will show how the prior assumption that the audio signals are approximately sparse in some time-frequency representation allows us to address the associated signal processing task.
Keywords :
Fourier transforms; audio coding; blind source separation; audio coding; blind source separation; signal processing; sparse representations; Audio coding; Councils; Discrete Fourier transforms; Discrete wavelet transforms; Fourier transforms; Multiple signal classification; Resonance; Signal analysis; Signal processing; Source separation; Audio coding; Fourier transforms; basis functions; discrete cosine transforms; music; signal representations; wavelet transforms;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2009.2030345