Multichannel Nonnegative Matrix Factorization Research Articles

A study on the target detection method of the continuous-wave active sonar in reverberation based on beamspace-domain multichannel nonnegative matrix factorization

A study on the target detection method of the continuous-wave active sonar in reverberation based on beamspace-domain multichannel nonnegative matrix factorization

Separation of Moving Sound Sources Using Multichannel NMF and Acoustic Tracking

In this paper, we propose a method for separation of moving sound sources. The method is based on first tracking the sources and then estimation of source spectrograms using multichannel nonnegative matrix factorization (NMF) and extracting the sources from the mixture by single-channel Wiener filtering. We propose a novel multichannel NMF model with time-varying mixing of the sources denoted by spatial covariance matrices (SCM) and provide update equations for optimizing model parameters minimizing squared Frobenius norm. The SCMs of the model are obtained based on estimated directions of arrival of tracked sources at each time frame. The evaluation is based on established objective separation criteria and using real recordings of two and three simultaneous moving sound sources. The compared methods include conventional beamforming and ideal ratio mask separation. The proposed method is shown to exceed the separation quality of other evaluated blind approaches according to all measured quantities. Additionally, we evaluate the method's susceptibility toward tracking errors by comparing the separation quality achieved using annotated ground truth source trajectories.

Multi-channel non-negative matrix factorization with binary mask initialization for automatic speech recognition

Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of acoustics, multichannel expansion has been proposed to consider spatial information for sound source separation. Conventional multi-channel NMF has a difficulty in an initial-value dependency of the separation performance due to local minima. This paper proposes initial value settings by using binary masking based sound source separation whose masks on the time frequency domain are calculated from the time difference of arrival of each source. The proposed method calculates initial spatial correlation matrices using separated sources by binary masking. The music separation experiments confirmed that the separation performance of the proposed method was better than that of the conventional method. In addition, we evaluated initial value settings by using binary masking for automatic speech recognition (ASR) tasks in noisy environments. The ASR experiments confirmed that appropriate initi...

Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization

This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been proposed for IVA. However, since the source model in IVA is based on a spherical multivariate distribution, IVA cannot utilize specific spectral structures such as the harmonic structures of pitched instrumental sounds. To solve this problem, we introduce NMF decomposition as the source model in IVA to capture the spectral structures. The formulation of the proposed method is derived from conventional multichannel NMF (MNMF), which reveals the relationship between MNMF and IVA. The proposed method can be optimized by the update rules of IVA and single-channel NMF. Experimental results show the efficacy of the proposed method compared with IVA and MNMF in terms of separation accuracy and convergence speed.

ランク1空間モデル制約付き多チャネルNMFを用いた柔軟索状ロボットにおける雑音抑圧

A hose-shaped rescue robot is one of the robots that are developed for disaster response in case of a large-scale disasters such as a great earthquake. The robot is suitable for entering narrow and dark places covered with rubble in the disaster site, and for finding inside it. This robot can transmit the ambient sound to its operator by using the built-in microphones. However, there is a serious problem that the inherent noise of this robot, such as the vibration sound or the fricative sound, is mixed into the transmitting voice, therefore disturbing the operator's hearing for a call of help from the victim of the disaster. In this paper, we apply the multichannel NMF (nonnegative matrix factorization) with the rank-1 spatial constraint (Rank-1 MNMF), which was proposed by Kitamura et al., to the reduction of the inherent noise.

빔공간-영역 다채널 비음수 행렬 분해 알고리즘을 이용한 음원 분리 기법 Part I: 빔공간-영역 다채널 비음수 행렬 분해 시스템

본 논문에서는 다채널 음향 신호의 음원 분리를 수행하기 위하여, 빔공간-영역에서 다채널 비음수 행렬 분해 기법을 이용하는 음원 분리 시스템을 제안한다. 비음수 행렬 분해(NMF) 기법은 음원 분리에서 최근 널리 쓰이는 알고리즘이며, 특히 최근에는 다채널 비음수 행렬 분해(MC-NMF) 기법으로 발전하여 다채널 음향 신호에 대해서 적용되고 있다. 본 논문에서 제안하는 다채널 비음수 행렬 분해 기법은 빔공간-영역에서 수행되어, 기존의 다채널 비음수 행렬 분해 기법에 비해 좋은 성능을 가진다. 제안되는 비음수 행렬 분해 기법은 SiSEC 2010의 데이터셋을 이용하여 검증되었다. In this paper, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel non-negative matrix factorization (NMF) method. The NMF algorithm is a famous algorithm which is used to solve the source separation problems. In this paper, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010) for evaluation.

빔공간-영역 다채널 비음수 행렬 분해 알고리즘을 이용한 음원 분리 기법 Part II: 빔공간-변환 기법에 대한 고찰

빔공간 변환(beamspace transform) 기법은 공간 영역의 신호를 입사각 혹은 그 사인함수의 영역으로 변환하는 기법으로, MUSIC과 같은 음원 정위 및 추적(source localization and tracking) 문제나 적응 빔형성(adaptive beamforming)과 같은 문제에서 많이 사용되는 기법이다. 다채널 음원 분리 기법에 사용될 때에는, 음원의 정보 뿐만아니라 해당 음원의 이미지(image)를 재구성하여야 하므로 역변환 기법 또한 중요하다. 본 논문에서는 멀티 채널 음원 분리 기법을 위한 빔공간 변환 기법과 그 역변환 기법에 대하여 고찰하였으며, 특히 빔공간-영역 다채널 비음수 행렬 분해 기법에 적용되었을 때 그 성능에 미치는 영향을 중점적으로 살펴보았다. Beamspace transform algorithm transforms spatial-domain data - such as x, y, z dimension - into incidence-angle-domain data, which is called beamspace-domain data. The beamspace transform method is generally used in source localization and tracking, and adaptive beamforming problem. When the beamspace transform method is used in multichannel audio source separation, the inverse beamspace transform is also important because the source image have to be reconstructed. This paper studies the beamspace transform and inverse transform algorithms for multichannel audio source separation system, especially for the beamspace-domain multichannel NMF algorithm.

Beamspace-Domain Multichannel Nonnegative Matrix Factorization for Audio Source Separation

In this letter, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel nonnegative matrix factorization (NMF) method. The conventional multichannel NMF algorithm performs well with multichannel mixing data, but there is still room for enhancement in multichannel real-world recording data. In this letter, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to 2-channel and 4-channel unsupervised audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010). Our algorithm shows a better performance than the conventional NMF method in an evaluation results.

Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation

We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and convolutive mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008).

Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation

We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the s...