arXiv subjects

Nobutaka Ito

Publications and source records attributed to Nobutaka Ito.

10 recordsLinked to original sources

arXiv2026

cSTMM: A Unified Complex Spherical Student's $t$ Mixture Model for Directional Statistics in Mask-Based Blind Speech Separation

Directional-statistics-based mask-based blind speech separation (BSS) clusters normalized time-frequency (TF) observations from $M$ microphones on the complex unit sphere, without relying on plane-wave or spherical-wave assumptions. Existing methods use separately defined angular mixture models, which makes the effect of density shape difficult to isolate. This paper proposes a complex spherical Student's $t$ mixture model (cSTMM) that connects the complex angular central Gaussian mixture model (cACGMM), complex Bingham mixture model (cBMM), and complex Watson mixture model (cWMM) through the degrees of freedom $ν$ and eigenvalue constraints. We derive a latent-scale expectation-maximization (EM) framework with an approximate M-step based on high-concentration approximation (HCA). On noise-free LibriSpeech mixtures reverberated using measured room impulse responses (RIRs), the development-selected value $ν^\ast=1$ outperformed the cACGMM-equivalent choice $ν=M$ in all 18 test conditions, yielding a mean signal-to-distortion ratio improvement (SDRi) gain of $0.25\,\mathrm{dB}$. The model reduces to the cACGMM at $ν=M$ and approaches the cBMM/cWMM in the large-$ν$ limits.

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arXiv2026

Subspace Track-before-Detect for Passive Multi-Target Tracking with Unknown Emitted Signals

Passive multi-target tracking (MTT) aims to infer the time-varying kinematic and activity states of an unknown number of sources that emit unknown and possibly nonstationary signals, using only noisy mixtures of these signals observed at sensors. Track-before-detect (TBD) methods improve noise robustness by evaluating multi-target hypotheses directly on raw sensor data, without relying on a preceding detection stage. However, existing TBD likelihoods typically assume that the contribution of each active target to the observation is determined solely by its kinematic state. This assumption does not hold in passive sensing scenarios, where the observed mixtures also depend on unknown and possibly nonstationary source signals. To address this issue, we propose subspace TBD, a passive multi-target TBD method that employs a source-signal-insensitive likelihood derived from the complex spherical Student's $t$ (cST) distribution. Instead of explicitly modeling or estimating the nuisance source signals, the method represents each multi-target hypothesis by the subspace spanned by source steering vectors. The cST likelihood then evaluates how well the normalized multichannel mixtures align with this subspace. We conducted acoustic MTT simulations with two moving speakers in noisy, reverberant environments, comparing the proposed method with a baseline consisting of steered response power with phase transform (SRP-PHAT) followed by a sequential Monte Carlo implementation of the generalized labeled multi-Bernoulli filter (SMC-GLMB). The proposed method achieved lower mean optimal subpattern assignment (OSPA) values in all tested conditions.

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arXiv2026

Fast Multichannel NMF with Block-Diagonal Spatial Covariance Matrices for Efficient Blind Source Separation Using Distributed Microphone Arrays

Distributed microphone arrays composed of multiple subarrays enable blind source separation over a wide spatial area. Directly applying fast multichannel nonnegative matrix factorization (FastMNMF) to all subarrays can exploit observations from all subarrays, but it requires repeated inversions of large matrices spanning all microphones, causing the computational cost to increase rapidly as the number of microphones grows. In contrast, applying FastMNMF to one subarray reduces the matrix size but cannot exploit observations from other subarrays. We propose distributed FastMNMF, which imposes a block-diagonal structure on the source spatial covariance matrices, so that matrix inversions are performed within subarrays. The NMF-based source spectrogram model is shared across subarrays, allowing the method to aggregate source activity information while discarding inter-subarray covariance. In synchronized, noiseless simulations with fixed room and array/source geometry, the method required less computation time than conventional FastMNMF using all subarrays, achieved a higher average source-to-distortion ratio than conventional FastMNMF using one subarray, and was applicable in the tested five-source condition, where each four-microphone subarray was locally underdetermined.

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arXiv2025

30+ Years of Source Separation Research: Achievements and Future Challenges

Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review the major contributions and advancements in the past three decades in the speech, audio, and music SS research field. We will cover both single- and multi-channel SS approaches. We will also look back on key efforts to foster a culture of scientific evaluation in the research field, including challenges, performance metrics, and datasets. We will conclude by discussing current trends and future research directions.

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arXiv2023

Audio Signal Enhancement with Learning from Positive and Unlabelled Data

Supervised learning is a mainstream approach to audio signal enhancement (SE) and requires parallel training data consisting of both noisy signals and the corresponding clean signals. Such data can only be synthesised and are mismatched with real data, which can result in poor performance on real data. Moreover, clean signals may be inaccessible in certain scenarios, which renders this conventional approach infeasible. Here we explore SE using non-parallel training data consisting of noisy signals and noise, which can be easily recorded. We define the positive (P) and the negative (N) classes as signal inactivity and activity, respectively. We observe that the spectrogram patches of noise clips can be used as P data and those of noisy signal clips as unlabelled data. Thus, learning from positive and unlabelled data enables a convolutional neural network to learn to classify each spectrogram patch as P or N to enable SE.

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arXiv2021

A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter

This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the underdetermined case). When there are as many sources as microphones (i.e., the determined case), BSS can be performed computationally efficiently by independent component analysis (ICA). Unfortunately, however, ICA is basically inapplicable to the underdetermined case. Another BSS approach using the multichannel Wiener filter (MWF) is applicable even to this case, and encompasses full-rank spatial covariance analysis (FCA) and multichannel non-negative matrix factorization (MNMF). However, these methods require massive numbers of matrix inversions to design the MWF, and are thus computationally inefficient. To overcome this drawback, we exploit the well-known property of diagonal matrices that matrix inversion amounts to mere inversion of the diagonal elements and can thus be performed computationally efficiently. This makes it possible to drastically reduce the computational cost of the above matrix inversions based on a joint diagonalization (JD) idea, leading to computationally efficient BSS. Specifically, we restrict the N spatial covariance matrices (SCMs) of all N sources to a class of (exactly) jointly diagonalizable matrices. Based on this approach, we present FastFCA, a computationally efficient extension of FCA. We also present a unified framework for underdetermined and determined audio BSS, which highlights a theoretical connection between FastFCA and other methods. Moreover, we reveal that FastFCA can be regarded as a regularized version of approximate joint diagonalization (AJD).

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arXiv2020

A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise

This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all targets. Many conventional tracking methods rely on preprocessing for detection (e.g., thresholding), which severely limits tracking performance at a low signal-to-noise ratio (SNR). In contrast, the proposed method operates directly on raw sensor signals without requiring such preprocessing. Though there also exist methods applicable to raw sensor signals called track-before-detect, the proposed method has significant advantages over them. First, it is general without any restrictions on observation/process noise statistics (e.g., Gaussian) or the functional form of each target's contribution to the sensors (e.g., linear, separable, binary). Especially, it includes Salmond et al.'s track-before-detect particle filter for a single target as a particular example up to some implementation details. Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to a target birth/death model. We present a simulation example of radio-frequency tomography, where it significantly outperformed Nannuru et al.'s state-of-the-art method based on random finite sets in terms of the optimal subpattern assignment (OSPA) metric.

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arXiv2018

The 2018 Signal Separation Evaluation Campaign

This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018). This year's edition was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning based systems. For this purpose, we prepared a new music separation database: MUSDB18, featuring close to 10h of audio. Additionally, open-source software was released to automatically load, process and report performance on MUSDB18. Furthermore, a new official Python version for the BSSEval toolbox was released, along with reference implementations for three oracle separation methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter. We finally report the results obtained by the participants.

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arXiv2018

FastFCA-AS: Joint Diagonalization Based Acceleration of Full-Rank Spatial Covariance Analysis for Separating Any Number of Sources

Here we propose FastFCA-AS, an accelerated algorithm for Full-rank spatial Covariance Analysis (FCA), which is a robust audio source separation method proposed by Duong et al. ["Under-determined reverberant audio source separation using a full-rank spatial covariance model," IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830-1840, Sept. 2010]. In the conventional FCA, matrix inversion and matrix multiplication are required at each time-frequency point in each iteration of an iterative parameter estimation algorithm. This causes a heavy computational load, thereby rendering the FCA infeasible in many applications. To overcome this drawback, we take a joint diagonalization approach, whereby matrix inversion and matrix multiplication are reduced to mere inversion and multiplication of diagonal entries. This makes the FastFCA-AS significantly faster than the FCA and even applicable to observed data of long duration or a situation with restricted computational resources. Although we have already proposed another acceleration of the FCA for two sources, the proposed FastFCA-AS is applicable to an arbitrary number of sources. In an experiment with three sources and three microphones, the FastFCA-AS was over 420 times faster than the FCA with a slightly better source separation performance.

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arXiv2018

FastFCA: A Joint Diagonalization Based Fast Algorithm for Audio Source Separation Using A Full-Rank Spatial Covariance Model

A source separation method using a full-rank spatial covariance model has been proposed by Duong et al. ["Under-determined Reverberant Audio Source Separation Using a Full-rank Spatial Covariance Model," IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830-1840, Sep. 2010], which is referred to as full-rank spatial covariance analysis (FCA) in this paper. Here we propose a fast algorithm for estimating the model parameters of the FCA, which is named Fast-FCA, and applicable to the two-source case. Though quite effective in source separation, the conventional FCA has a major drawback of expensive computation. Indeed, the conventional algorithm for estimating the model parameters of the FCA requires frame-wise matrix inversion and matrix multiplication. Therefore, the conventional FCA may be infeasible in applications with restricted computational resources. In contrast, the proposed FastFCA bypasses matrix inversion and matrix multiplication owing to joint diagonalization based on the generalized eigenvalue problem. Furthermore, the FastFCA is strictly equivalent to the conventional algorithm. An experiment has shown that the FastFCA was over 250 times faster than the conventional algorithm with virtually the same source separation performance.

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