4 research outputs found
A general framework for online audio source separation
We consider the problem of online audio source separation. Existing
algorithms adopt either a sliding block approach or a stochastic gradient
approach, which is faster but less accurate. Also, they rely either on spatial
cues or on spectral cues and cannot separate certain mixtures. In this paper,
we design a general online audio source separation framework that combines both
approaches and both types of cues. The model parameters are estimated in the
Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation
(GEM) algorithm with multiplicative updates. The separation performance is
evaluated as a function of the block size and the step size and compared to
that of an offline algorithm.Comment: International conference on Latente Variable Analysis and Signal
Separation (2012
Combining blockwise and multi-coefficient stepwise approches in a general framework for online audio source separation
This article considers the problem of online audio source separation. Various algorithms can be found in the literature, featuring either blockwise or stepwise approaches, and using either the spectral or spatial characteristics of the sound sources of a mixture. We offer an algorithm that can combine both stepwise and blockwise approaches, and that can use spectral and spatial information. We propose a method for pre-processing the data of each block and offer a way to deduce an Equivalent Rectangular Bandwith time-frequency representation out of a Short-Time Fourier Transform. The efficiency of our algorithm is then tested for various parameters and the effect of each of those parameters on the quality of separation and on the computation time is then discussed