arXiv subjects

Kouei Yamaoka

Publications and source records attributed to Kouei Yamaoka.

6 recordsLinked to original sources

arXiv2026

Audio Spotforming via Post-Filtering Using Cross-Array Non-target Estimates

Audio spotforming is a technique for extracting target speech from noisy mixtures by utilizing multiple microphone arrays. Conventional methods estimate a shared target speech component from linearly separated signals obtained by each array using low-rank approximations and apply post filtering (PF) based on this estimated low-rank representation. However, owing to the mismatch between low-rank models and the complex structure of speech signals, directly relying on low-rank approximations for PF can degrade the speech extraction performance. In this study, we leverage the observation that non-target components located in the target speech direction from the perspective of one array can be spatially separated when viewed from other arrays. This insight motivates a new spotforming method for efficient post-filter estimation using non-target estimates across arrays instead of relying on low-rank approximations. Experiments demonstrate that the proposed method outperforms conventional spotforming methods.

cs.SD↗

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.

eess.AS↗

arXiv2025

Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions

Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-source conditions and cannot fully capture spatial information. The central challenge in modeling spatial information lies in multi-source conditions, where the correct correspondence between each sound source and its location is required. To tackle this problem, we propose Spatial-CLAP, which introduces a content-aware spatial encoder that enables spatial representations coupled with audio content. We further propose spatial contrastive learning (SCL), a training strategy that explicitly enforces the learning of the correct correspondence and promotes more reliable embeddings under multi-source conditions. Experimental evaluations, including downstream tasks, demonstrate that Spatial-CLAP learns effective embeddings even under multi-source conditions, and confirm the effectiveness of SCL. Moreover, evaluation on unseen three-source mixtures highlights the fundamental distinction between conventional single-source training and our proposed multi-source training paradigm. These findings establish a new paradigm for spatially-aware audio--text embeddings.

cs.SD↗

arXiv2025

Incremental Averaging Method to Improve Graph-Based Time-Difference-of-Arrival Estimation

Estimating the position of a speech source based on time-differences-of-arrival (TDOAs) is often adversely affected by background noise and reverberation. A popular method to estimate the TDOA between a microphone pair involves maximizing a generalized cross-correlation with phase transform (GCC-PHAT) function. Since the TDOAs across different microphone pairs satisfy consistency relations, generally only a small subset of microphone pairs are used for source position estimation. Although the set of microphone pairs is often determined based on a reference microphone, recently a more robust method has been proposed to determine the set of microphone pairs by computing the minimum spanning tree (MST) of a signal graph of GCC-PHAT function reliabilities. To reduce the influence of noise and reverberation on the TDOA estimation accuracy, in this paper we propose to compute the GCC-PHAT functions of the MST based on an average of multiple cross-power spectral densities (CPSDs) using an incremental method. In each step of the method, we increase the number of CPSDs over which we average by considering CPSDs computed indirectly via other microphones from previous steps. Using signals recorded in a noisy and reverberant laboratory with an array of spatially distributed microphones, the performance of the proposed method is evaluated in terms of TDOA estimation error and 2D source position estimation error. Experimental results for different source and microphone configurations and three reverberation conditions show that the proposed method considering multiple CPSDs improves the TDOA estimation and source position estimation accuracy compared to the reference microphone- and MST-based methods that rely on a single CPSD as well as steered-response power-based source position estimation.

eess.AS↗

arXiv2022

Joint Optimization of Sampling Rate Offsets Based on Entire Signal Relationship Among Distributed Microphones

In this paper, we propose to simultaneously estimate all the sampling rate offsets (SROs) of multiple devices. In a distributed microphone array, the SRO is inevitable, which deteriorates the performance of array signal processing. Most of the existing SRO estimation methods focused on synchronizing two microphones. When synchronizing more than two microphones, we select one reference microphone and estimate the SRO of each non-reference microphone independently. Hence, the relationship among signals observed by non-reference microphones is not considered. To address this problem, the proposed method jointly optimizes all SROs based on a probabilistic model of a multichannel signal. The SROs and model parameters are alternately updated to increase the log-likelihood based on an auxiliary function. The effectiveness of the proposed method is validated on mixtures of various numbers of speakers.

eess.AS↗

arXiv2022

Estimation of Consistent Time Delays in Subsample via Auxiliary-Function-Based Iterative Updates

In this paper, we propose a new algorithm for the estimation of multiple time delays (TDs). Since a TD is a fundamental spatial cue for sensor array signal processing techniques, many methods for estimating it have been studied. Most of them, including generalized cross correlation (CC)-based methods, focus on how to estimate a TD between two sensors. These methods can then be easily adapted for multiple TDs by applying them to every pair of a reference sensor and another one. However, these pairwise methods can use only the partial information obtained by the selected sensors, resulting in inconsistent TD estimates and limited estimation accuracy. In contrast, we propose joint optimization of entire TD parameters, where spatial information obtained from all sensors is taken into account. We also introduce a consistent constraint regarding TD parameters to the observation model. We then consider a multidimensional CC (MCC) as the objective function, which is derived on the basis of maximum likelihood estimation. To maximize the MCC, which is a nonconvex function, we derive the auxiliary function for the MCC and design efficient update rules. We additionally estimate the amplitudes of the transfer functions for supporting the TD estimation, where we maximize the Rayleigh quotient under the non-negative constraint. We experimentally analyze essential features of the proposed method and evaluate its effectiveness in TD estimation. Code will be available at https://github.com/onolab-tmu/AuxTDE.

eess.SP↗