In this chapter we describe a Bayesian approach to audio source separation. The approach relies on probabilistic modeling of sound sources as (sparse) linear combinations of atoms from a dictionary and Markov chain Monte Carlo (MCMC) inference. Several prior distributions are considered for the source expansion coefficients. We first consider independent and identically distributed (iid) genera...

A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for easy development of new source separation algorithms which are optimised for specific problems. In this framework, source separation algorithms are constucted around denoising procedures. The resulting algorithms can range from almost blind to highly speci...

In this work we present a new scenario of analyzing and separating linear mixtures of musical instrument signals. When instruments are playing in unison, traditional source separation methods are not performing well. Although the sources share the same pitch, they often still differ in their modulation frequency caused by vibrato and/or tremolo effects. In this paper we propose source separatio...

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. ...

In popular music, a cover version or cover song, or simply cover, is a new performance or recording of a previously recorded, by someone other than the original artist. However, it is impossible to retrieve a piece of single track for most of people. Therefore, my goal is to deliver a program that separates a record into several tracks, each corresponding to a meaningful source, which can be us...

Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multilayer perceptron trained with a Wasserstein-...

Audio signal source separation is an interesting task performed by humans. In this paper, we present a frequency grouping algorithm based on principles of harmonicity and dynamics: frequency components with a harmonic relation and similar dynamics belong to the same source. The grouping is demonstrated for a variety of sound mixtures.

A system for user-guided audio source separation is presented in this article. Following previous works on time-frequency music representations, the proposed User Interface allows the user to select the desired audio source, by means of the assumed fundamental frequency (F0) track of that source. The system then automatically refines the selected F0 tracks, estimates and separates the correspon...

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This article provides an overview of the first stereo audio source separation evaluation campaign, organized by the authors. Fifteen underdetermined stereo source separation algorithms have been applied to various audio data, including instantaneous, convolutive and real mixtures of speech or music sources. The data and the algorithms are presented and the estimated source signals are compared ...