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.
In this paper, we derive closed-form and near closed-form solutions for joint source and sensor localization from time-difference-of-arrival (TDOA) measurements. In our previous works, we derived ...closed-form and near closed-form solutions for joint source and sensor localization from time-of-arrival (TOA) measurements. On the basis of these results, the main idea in this paper is to recover the TOA information only from the given TDOA measurements. We show that the TOA information can be recovered by using the low-rank property of the difference of square TOA-distance matrix in a closed-form or a near closed-form based on the linear method of solving polynomial equations. Since the low-rank property is reliable even in noisy cases, the TOA recovery works well under both small and large amounts of noise. The root-mean-squared errors achieved by our proposed algorithms are compared with the Cramér-Rao lower bound in synthetic experiments. The results show that the proposed methods work well for both small and large amounts of noise and for small and large numbers of sources and sensors.
We study the problem of joint source and sensor localization from time-of-arrival (TOA) measurements. For the case of nine sources and four sensors (or, by symmetry, vice versa), we derive a ...closed-form solution. For the cases of eight and seven sources, we derive near closed-form solutions depending on one or two parameters, respectively. The main aim of our method is to transform the original localization problem to other problems such that the number of unknown parameters is as small as possible. Although the constraints in the problems are multivariate and quadratic polynomial equations, by using a linear method of solving polynomial equations we can find closed-form solutions and near closed-form solutions if the number of unknown parameters is small and the number of constraints is large. Unlike off-the-shelf methods for solving polynomial systems, such as the Grobner basis method, our solution uses only low-degree monomials. Thus, unlike these other methods, our solution is more stable to noise, as verified using the Cramer-Rao lower bound.
This paper presents new algorithms of independent component analysis (ICA) for super-Gaussian sources based on auxiliary function technique. The algorithms consist of two alternative updates: 1) ...update of demixing matrix and 2) update of weighted covariance matrix, which include no tuning parameters such as step size. The monotonic decrease of the objective function at each update is guaranteed. The experimental results show that the derived algorithms are robust to nonstationary data and outliers, and the convergence is faster than natural-gradient-based algorithm.
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impairments in social reciprocity and communication together with restricted interest and stereotyped ...behaviors. The Autism Diagnostic Observation Schedule (ADOS) is considered a 'gold standard' instrument for diagnosis of ASD and mainly depends on subjective assessments made by trained clinicians. To develop a quantitative and objective surrogate marker for ASD symptoms, we investigated speech features including F0, speech rate, speaking time, and turn-taking gaps, extracted from footage recorded during a semi-structured socially interactive situation from ADOS. We calculated not only the statistic values in a whole session of the ADOS activity but also conducted a block analysis, computing the statistical values of the prosodic features in each 8s sliding window. The block analysis identified whether participants changed volume or pitch according to the flow of the conversation. We also measured the synchrony between the participant and the ADOS administrator. Participants with high-functioning ASD showed significantly longer turn-taking gaps and a greater proportion of pause time, less variability and less synchronous changes in blockwise mean of intensity compared with those with typical development (TD) (p<0.05 corrected). In addition, the ASD group had significantly wider distribution than the TD group in the within-participant variability of blockwise mean of log F0 (p<0.05 corrected). The clinical diagnosis could be discriminated using the speech features with 89% accuracy. The features of turn-taking and pausing were significantly correlated with deficits of ASD in reciprocity (p<0.05 corrected). Additionally, regression analysis provided 1.35 of mean absolute error in the prediction of deficits in reciprocity, to which the synchrony of intensity especially contributed. The findings suggest that considering variance of speech features, interaction and synchrony with conversation partner are critical to characterize atypical features in the conversation of people with ASD.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper describes several important methods for the blind source separation of audio signals in an integrated manner. Two historically developed routes are featured. One started from independent ...component analysis and evolved to independent vector analysis (IVA) by extending the notion of independence from a scalar to a vector. In the other route, nonnegative matrix factorization (NMF) has been extended to multichannel NMF (MNMF). As a convergence point of these two routes, independent low-rank matrix analysis has been proposed, which integrates IVA and MNMF in a clever way. All the objective functions in these methods are efficiently optimized by majorization-minimization algorithms with appropriately designed auxiliary functions. Experimental results for a simple two-source two-microphone case are given to illustrate the characteristics of these five methods.
PDF
