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This paper proposes a new method of frequency-domain blind source separation (FD-BSS), able to separate acoustic sources in challenging conditions. In frequency-domain BSS, the time-domain signals are transformed into time-frequency series and the separation is generally performed by applying independent component analysis (ICA) at each frequency envelope. When short signals are observed and long...
A sound source separation method based on frequency-domain independent component analysis (FD-ICA) is proposed. This method fully utilizes the dodecahedral microphone array (DHMA), which has several merits: 1) the size of the array is very small and thus easy to handle; 2) the amplitude difference among microphones on the different surfaces is large; and 3) it is less affected by spatial aliasing...
This paper deals with the problem of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial properties of the source. We consider two covariance models and address the estimation of their parameters from the recorded mixture by a...
In our recent work an effective method for multiple source localization has been proposed under the name of cumulative state coherence transform (cSCT). Exploiting the physical meaning of the frequency-domain blind source separation and the sparse time-frequency dominance of the acoustic sources, multiple reliable TDOAs can be estimated with only two microphones, regardless of the permutation problem...
A novel method to solve the permutation problem for Blind Source Separation (BSS) is presented. According to the acoustic propagation model, in frequency-domain, each separation matrix can be represented with a set of states associated with each source. We formulate a novel transform of the states which is independent of the aliasing and of the permutations since states belonging to all the sources...
Independent component analysis (ICA) for convolutive mixtures is often applied in the frequency domain due to the desirable decoupling into independent instantaneous mixtures per frequency bin. This approach suffers from a well-known scaling and permutation ambiguity. Existing methods perform a computation-heavy and sometimes unreliable phase of post-processing which typically makes use of knowledge...
We present a new approach to independent component analysis (ICA) by extending the formulation of univariate source signals to multivariate source signals. The new approach is termed independent vector analysis (IVA). In the model, we assume that linear mixing model exists in each dimension separately, and the latent sources are independent of the others. In contrast to ICA, the sources are random...
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