considering the increase in waste production, scarcity of land allocated to landfill in urban areas, concerns about ecological damages caused by hazardous waste and damages done to the environment, municipal waste management has become of tremendous importance. the study investigated the behavioral patterns of households regarding waste separation at source using data collected through intervie...

Our group attracted participants from various research areas to discuss aspects of source separation for audio signals, performed either in a blind way or by using additional knowledge about the underlying sources or the mixing process. In many source separation approaches, one assumes that sources are independent, uncorrelated, and do not overlap with regard to a given representation. Also one...

Blind source separation of broad band signals in a multi-path environment remains a di cult problem. Robustness has been limited due to frequency permutation ambiguities. Increasing the number of sensors allows improved performance but introduces degrees of freedom in the separating lters that are not determined by separation criteria. We propose to further shape the lters and improve the robus...

This paper discusses some theoritical results on underdetermined source separation i.e. when the mixing matrix is degenerate, espcially when there is more sources than observations. In this case, we show that the sources can be restored up to an arbitrary additive random vector. In the particular case of discrete sources, very relevant for digital communications, we show that this vector is cer...

This paper deals with the general problem of separating two independent wideband sources when they are mixed by unknown filters. In order to solve this problem. a backward framework is proposed which is composed of two different stages. The fiist one consists of two linear predictors devoted to improve the source separation, whitening the input signals. Their coefficients are calculated applyin...

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. ...