• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.121-128
• /
• 2017

In this paper, we propose a dual-channel acoustic event detection (AED) method using nonnegative tensor factorization (NTF) and hidden Markov model (HMM) in order to improve detection accuracy of AED in multisource environments. The proposed method first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique applied to dual-channel input signals. After that, an HMM-based likelihood ratio test is carried out to verify the detected events by using channel gains. The detection accuracy of the proposed method is measured by F-measures under 9 different multisource conditions. Then, it is also compared with those of conventional AED methods such as Gaussian mixture model and nonnegative matrix factorization. It is shown from the experiments that the proposed method outperforms the convectional methods under all the multisource conditions.

• Journal of the Korean Mathematical Society
• /
• v.56 no.6
• /
• pp.1503-1514
• /
• 2019

A rank 1 matrix has a factorization as $uv^t$ for vectors u and v of some orders. The arctic rank of a rank 1 matrix is the half number of nonzero entries in u and v. A matrix of rank k can be expressed as the sum of k rank 1 matrices, a rank 1 decomposition. The arctic rank of a matrix A of rank k is the minimum of the sums of arctic ranks of the rank 1 matrices over all rank 1 decomposition of A. In this paper we obtain characterizations of the linear operators that strongly preserve the symmetric arctic ranks of symmetric matrices over nonnegative reals.

• Journal of applied mathematics & informatics
• /
• v.30 no.1_2
• /
• pp.201-210
• /
• 2012

A modified multiplicative update algorithms is presented for computing the nearest correlation matrix. The convergence property is analyzed in details and a sufficient condition is given to guarantee that the proposed approach will not breakdown. A number of numerical experiments show that the modified multiplicative updating algorithm is efficient, and comparable with the existing algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2015.07a
• /
• pp.418-420
• /
• 2015

본 논문은 베이지안 비음수 행렬 인수분해 (Bayesian nonnegative matrix factorization, BNMF) 기반의 음성 강화 기법에서 음성과 잡음 성분의 latent source 수에 따른 강화성능에 대해 서술한다. BNMF 기반의 음성 강화 기법은 입력 신호를 서브 신호들의 합으로 분해한 후, 잡음 성분을 제거하는 방식으로 그 성능이 기존의 NMF 기반의 방법들보다 우수한 것으로 알려져 있다. 그러나 많은 계산량과 latent source 의 수에 따라 성능의 차이가 있다는 단점이 있다. 이러한 단점을 개선하기 위해 본 논문에서는 BNMF 기반의 음성 강화 기법에서 최적의 latent source 개수를 찾기 위한 실험을 진행하였다. 실험은 잡음의 종류, 음성의 종류, 음성과 잡음의 latent source 의 개수, 그리고 SNR 을 바꿔가며 진행하였고, 성능 평가 방법으로 PESQ (perceptual evaluation of speech quality) 를 이용하였다. 실험 결과, 음성의 latent source 개수는 성능에 영향을 주지 않지만, 잡음의 latent source 개수는 많을수록 성능이 좋은 것으로 확인되었다.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.2
• /
• pp.120-130
• /
• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• Journal of applied mathematics & informatics
• /
• v.30 no.3_4
• /
• pp.365-380
• /
• 2012

Non-negative matrix factorization (NMF) is a very efficient method to explain the relationship between functions for finding basis information of multivariate nonnegative data. The multiplicative update (MU) algorithm is a popular approach to solve the NMF problem, but it fails to approach a stationary point and has inner iteration and zero divisor. So the elementwisely alternating projected gradient (eAPG) algorithm was proposed to overcome the defects. In this paper, we use the fact that the equilibrium point is stable to prove the convergence of the eAPG algorithm. By using a classic model, the equilibrium point is obtained and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the eAPG algorithm are obtained, which can accelerate the convergence. In addition, the conditions, which satisfy that the non-zero equilibrium point exists and is stable, can cause that the algorithm converges to different values. Both of them are confirmed in the experiments. And we give the mathematical proof that the eAPG algorithm can reach the appointed precision at the least iterations compared to the MU algorithm. Thus, we theoretically illustrate the advantages of the eAPG algorithm.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.2
• /
• pp.123-129
• /
• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.34-36
• /
• 2006

In this paper we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor Imagery EEG data in BCI competition 2003. show that the method indeed finds meaningful EEG features automatically, while some existing methods should undergo cross-validation to find them.

• Genomics & Informatics
• /
• v.4 no.1
• /
• pp.33-39
• /
• 2006

Alternative splicing (AS) is an important mechanism of producing transcriptome diversity and microarray techniques are being used increasingly to monitor the splice variants. There exist three types of microarrays interrogating AS events-junction, exon, and tiling arrays. Junction probes have the advantage of monitoring the splice site directly. Johnson et al., performed a genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays (Science 302:2141-2144, 2003), which monitored splicing at every known exon-exon junctions for more than 10,000 multi-exon human genes in 52 tissues and cell lines. Here, we describe an algorithm to deduce the relative concentration of isoforms from the junction array data. Non-negative Matrix Factorization (NMF) is applied to obtain the transcript structure inferred from the expression data. Then we choose the transcript models consistent with the ECgene model of alternative splicing which is based on mRNA and EST alignment. The probe-transcript matrix is constructed using the NMF-consistent ECgene transcripts, and the isoform abundance is deduced from the non-negative least squares (NNLS) fitting of experimental data. Our method can be easily extended to other types of microarrays with exon or junction probes.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.5150-5155
• /
• 2011

Alzheimer's disease (AD) and vascular dementia (VD) are the most common dementia. In this paper, we proposed a region selection for classification of AD, VD and normal (NOR) based on micro-Raman spectra from platelet. The preprocessing step is a smoothing followed by background elimination to the original spectra. Then we applied the minmax method for normalization. After the inspection of the preprocessed spectra, we found that 725-777, 1504-1592 and 1632-1700 $cm^{-1}$ regions are the most discriminative features in AD, VD and NOR spectra. We applied the feature transformation using PCA (principal component analysis) and NMF (nonnegative matrix factorization). The classification result of MAP(maximum a posteriori probability) involving 327 spectra transformed features using proposed local region showed about 92.8 % true classification average rate.