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We propose a new approach for clustering competing speech sources using distributed microphone arrays. In this approach, we first define two feature vectors where the first captures the intra-node location information while the second captures the level difference of speech energy recorded at different nodes. Then, we introduce Watson and Dirichlet mixture models to model the first and second features,...
This paper proposes a novel blind compensation of sampling frequency mismatch for asynchronous microphone array. Digital signals simultaneously observed by different recording devices have drift of the time differences between the observation channels because of the sampling frequency mismatch among the devices. Based on the model that such the time difference is constant within each time frame, but...
Most of the binaural source separation algorithms only consider the dissimilarities between the recorded mixtures such as interaural phase and level differences (IPD, ILD) to classify and assign the time-frequency (T-F) regions of the mixture spectrograms to each source. However, in this paper we show that the coherence between the left and right recordings can provide extra information to label the...
REPET-SIM is a generalization of the REpeating Pattern Extraction Technique (REPET) that uses a similarity matrix to separate the repeating background from the non-repeating foreground in a mixture. The method assumes that the background (typically the music accompaniment) is dense and low-ranked, while the foreground (typically the singing voice) is sparse and varied. While this assumption is often...
In Blind Source Separation, or BSS, a set of source signals are recovered from a set of mixed observations without knowledge of the mixing parameters. Originated for real signals, BSS has recently been applied to finite fields, enabling more practical applications. However, classical entropy-based techniques do not perform well in finite fields. Here, we propose a non-linear encoding of the sources...
We propose a new method to separate mass spectra into components of each chemical compound for explosives detection. The conventional method based on probabilistic latent component analysis (PLCA) is effective because the method can solve the problems of non-negativity and non-orthogonality by using sparsity of the domain of explosives detection. However, the convergence of the method is slow, and...
We present a novel modification to the well-known infomax algorithm of blind source separation. Under natural gradient descent, the infomax algorithm converges to a stationary point of a limiting ordinary differential equation. However, due to the presence of saddle points or local minima of the corresponding likelihood function, the algorithm may be trapped around these “bad” stationary points for...
We propose permutation-free frequency-domain blind source separation (BSS) via full-band clustering of the time-frequency (T-F) components based on time-varying signal presence priors. Frequency-domain methods of BSS usually process each frequency bin separately, and therefore necessitate the subsequent alignment of the permutation ambiguity that arises between frequency bins. In contrast, the proposed...
Since in many blind source separation applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both non-Gaussianity and sample dependency. In this paper, we use the Markov model to construct a general framework for the analysis and derivation of algorithms that take both properties into account. We also present two algorithms using two effective source...
This paper considers the estimation of time difference of arrival (TDOA) of multiple sparse sources when the number of sources is larger than that of the microphones. White Gaussian noise is assumed present at the microphone in addition to the instantaneously mixed sources. The TDOA estimate is obtained based on a maximum likelihood (ML) criteria, and the likelihood is obtained by marginalizing the...
This paper presents an efficient method for blind source separation of convolutively mixed speech signals. The method follows the popular frequency-domain approach, wherein researchers are faced with two main problems, namely, per-frequency mixing system estimation, and permutation alignment of source components at all frequencies. We adopt a novel concept, where we utilize local sparsity of speech...
We address the problem of blind separation of speech signals with a microphone array. We demonstrate that a signal propagating towards the array at an angle corresponds to interchannel phase difference (IPD) data that lies on a wrapped line (i.e helix) in a circular-linear domain. Thus, the problem reduces to that of fitting helices to data that lies on a cylinder. However, outliers abound because...
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