arXiv subjects

Rintaro Ikeshita

Publications and source records attributed to Rintaro Ikeshita.

22 records · Page 2Linked to original sources

arXiv2020

Jointly optimal denoising, dereverberation, and source separation

This paper proposes methods that can optimize a Convolutional BeamFormer (CBF) for jointly performing denoising, dereverberation, and source separation (DN+DR+SS) in a computationally efficient way. Conventionally, cascade configuration composed of a Weighted Prediction Error minimization (WPE) dereverberation filter followed by a Minimum Variance Distortionless Response beamformer has been usedas the state-of-the-art frontend of far-field speech recognition, however, overall optimality of this approach is not guaranteed. In the blind signal processing area, an approach for jointly optimizing dereverberation and source separation (DR+SS) has been proposed, however, this approach requires huge computing cost, and has not been extended for application to DN+DR+SS. To overcome the above limitations, this paper develops new approaches for jointly optimizing DN+DR+SS in a computationally much more efficient way. To this end, we first present an objective function to optimize a CBF for performing DN+DR+SS based on the maximum likelihood estimation, on an assumption that the steering vectors of the target signals are given or can be estimated, e.g., using a neural network. This paper refers to a CBF optimized by this objective function as a weighted Minimum-Power Distortionless Response (wMPDR) CBF. Then, we derive two algorithms for optimizing a wMPDR CBF based on two different ways of factorizing a CBF into WPE filters and beamformers. Experiments using noisy reverberant sound mixtures show that the proposed optimization approaches greatly improve the performance of the speech enhancement in comparison with the conventional cascade configuration in terms of the signal distortion measures and ASR performance. It is also shown that the proposed approaches can greatly reduce the computing cost with improved estimation accuracy in comparison with the conventional joint optimization approach.

eess.AS↗

arXiv2020

Overdetermined independent vector analysis

We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources $K$ is less than that of microphones $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that need not to be extracted. Independent vector analysis (IVA) can solve the problem by separating into $M$ sources and selecting the top $K$ highly nonstationary signals among them, but this approach suffers from a waste of computation especially when $K \ll M$. Channel reductions in preprocessing of IVA by, e.g., principle component analysis have the risk of removing the target signals. We here extend IVA to resolve these issues. One such extension has been attained by assuming the orthogonality constraint (OC) that the sample correlation between the target and noise signals is to be zero. The proposed IVA, on the other hand, does not rely on OC and exploits only the independence between sources and the stationarity of the noises. This enables us to develop several efficient algorithms based on block coordinate descent methods with a problem specific acceleration. We clarify that one such algorithm exactly coincides with the conventional IVA with OC, and also explain that the other newly developed algorithms are faster than it. Experimental results show the improved computational load of the new algorithms compared to the conventional methods. In particular, a new algorithm specialized for $K = 1$ outperforms the others.

eess.AS↗

arXiv2020

Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution

In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a state-of-the-art BSS method that enables us to take interfrequency correlations into account, but the generative model is limited within the multivariate Gaussian distribution and its parameter optimization algorithm does not guarantee stable convergence. To resolve these problems, first, we propose to extend the generative model to a parametric multivariate Student's t distribution that can deal with various types of signal. Secondly, we derive a new parameter optimization algorithm that guarantees the monotonic nonincrease in the cost function, providing stable convergence. Experimental results reveal that the cost function in the conventional IPSDTA does not display monotonically nonincreasing properties. On the other hand, the proposed method guarantees the monotonic nonincrease in the cost function and outperforms the conventional ILRMA and IPSDTA in the source-separation performance.

cs.SD↗

arXiv2016

Count Matroids of Group-Labeled Graphs

A graph $G=(V,E)$ is called $(k,\ell)$-sparse if $|F|\leq k|V(F)|-\ell$ for any nonempty $F\subseteq E$, where $V(F)$ denotes the set of vertices incident to $F$. It is known that the family of the edge sets of $(k,\ell)$-sparse subgraphs forms the family of independent sets of a matroid, called the $(k,\ell)$-count matroid of $G$. In this paper we shall investigate lifts of the $(k,\ell)$-count matroid by using group labelings on the edge set. By introducing a new notion called near-balancedness, we shall identify a new class of matroids, where the independence condition is described as a count condition of the form $|F|\leq k|V(F)|-\ell +α_ψ(F)$ for some function $α_ψ$ determined by a given group labeling $ψ$ on $E$.

math.CO↗