579 research outputs found
Approximate Message Passing for Underdetermined Audio Source Separation
Approximate message passing (AMP) algorithms have shown great promise in
sparse signal reconstruction due to their low computational requirements and
fast convergence to an exact solution. Moreover, they provide a probabilistic
framework that is often more intuitive than alternatives such as convex
optimisation. In this paper, AMP is used for audio source separation from
underdetermined instantaneous mixtures. In the time-frequency domain, it is
typical to assume a priori that the sources are sparse, so we solve the
corresponding sparse linear inverse problem using AMP. We present a block-based
approach that uses AMP to process multiple time-frequency points
simultaneously. Two algorithms known as AMP and vector AMP (VAMP) are evaluated
in particular. Results show that they are promising in terms of artefact
suppression.Comment: Paper accepted for 3rd International Conference on Intelligent Signal
Processing (ISP 2017
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Gaussian process (GP) audio source separation is a time-domain approach that
circumvents the inherent phase approximation issue of spectrogram based
methods. Furthermore, through its kernel, GPs elegantly incorporate prior
knowledge about the sources into the separation model. Despite these compelling
advantages, the computational complexity of GP inference scales cubically with
the number of audio samples. As a result, source separation GP models have been
restricted to the analysis of short audio frames. We introduce an efficient
application of GPs to time-domain audio source separation, without compromising
performance. For this purpose, we used GP regression, together with spectral
mixture kernels, and variational sparse GPs. We compared our method with
LD-PSDTF (positive semi-definite tensor factorization), KL-NMF
(Kullback-Leibler non-negative matrix factorization), and IS-NMF (Itakura-Saito
NMF). Results show that the proposed method outperforms these techniques.Comment: Paper submitted to the 44th International Conference on Acoustics,
Speech, and Signal Processing, ICASSP 2019. To be held in Brighton, United
Kingdom, between May 12 and May 17, 201
Improved Convolutive and Under-Determined Blind Audio Source Separation with MRF Smoothing
Convolutive and under-determined blind audio source separation from noisy recordings is a challenging problem. Several computational strategies have been proposed to address this problem. This study is concerned with several modifications to the expectation-minimization-based algorithm, which iteratively estimates the mixing and source parameters. This strategy assumes that any entry in each source spectrogram is modeled using superimposed Gaussian components, which are mutually and individually independent across frequency and time bins. In our approach, we resolve this issue by considering a locally smooth temporal and frequency structure in the power source spectrograms. Local smoothness is enforced by incorporating a Gibbs prior in the complete data likelihood function, which models the interactions between neighboring spectrogram bins using a Markov random field. Simulations using audio files derived from stereo audio source separation evaluation campaign 2008 demonstrate high efficiency with the proposed improvement
Hybrid model and structured sparsity for under-determined convolutive audio source separation
International audienceWe consider the problem of extracting the source signals from an under-determined convolutive mixture, assuming known filters. We start from its formulation as a minimization of a convex functional, combining a classical discrepancy term between the observed mixture and the one reconstructed from the estimated sources, and a sparse regularization term of source coefficients in a time-frequency domain. We then introduce a first kind of structure, using a hybrid model. Finally, we embed the previously introduced Windowed-Group-Lasso operator into the iterative thresholding/shrinkage algorithm, in order to take into account some structures inside each layers of time-frequency representations. Intensive numerical studies confirm the benefits of such an approach
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