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This paper proposes novel methods for extracting a single Speech signal of Interest (SOI) from a multichannel observed signal in underdetermined situations, i.e., when the observed signal contains more speech signals than microphones. It focuses on extracting the SOI using prior knowledge of the SOI's Direction of Arrival (DOA). Conventional beamformers (BFs) and Blind Source Separation (BSS) with spatial regularization struggle to suppress interference speech signals in such situations. Although Switching Minimum Power Distortionless Response BF (Sw-MPDR) can handle underdetermined situations using a switching mechanism, its estimation accuracy significantly decreases when it relies on a steering vector determined by the SOI's DOA. Spatially-Regularized Independent Vector Extraction (SRIVE) can robustly enhance the SOI based solely on its DOA using spatial regularization, but its performance degrades in underdetermined situations. This paper extends these conventional methods to overcome their limitations. First, we introduce a time-varying Gaussian (TVG) source model to Sw-MPDR to effectively enhance the SOI based solely on the DOA. Second, we introduce the switching mechanism to SRIVE to improve its speech enhancement performance in underdetermined situations. These two proposed methods are called Switching weighted MPDR (Sw-wMPDR) and Switching SRIVE (Sw-SRIVE). We experimentally demonstrate that both surpass conventional methods in enhancing the SOI using the DOA in underdetermined situations. [ABSTRACT FROM AUTHOR]

Allotment gardens (AGs) are widely used in metropolitan areas around the world to offer agricultural opportunities to urban residents. However, there are not enough individual plots for residents to rent for urban gardening, and research on AGs from a city-wide perspective is ongoing. In addition, AGs have a long history in Tokyo, yet few international studies on the current situation of AGs have addressed Asian cities. Thus, this study intends to analyze the provision of AGs and its influencing factors in Tokyo. Using ArcGIS combined the 472-points dataset created by geo-coordinate mapping with urban GIS data to reveal spatial characteristics in four dimensions. Results demonstrate that most AGs are in the urbanization promotion area; most municipalities have AGs; AGs are concentrated within 20 to 30 km from the center of Tokyo; the AGs' clusters are located at the municipal boundaries. We conducted multiple regressions to determine the influencing factors at the municipal level, with the provision that AGs are related to population density, land price, and the ratio of productive green space. The policy implication of this study is that policymakers need to consider the siting strategy of AGs based on spatial characteristics of AGs. [ABSTRACT FROM AUTHOR]

We propose a beamforming method that minimizes the $\ell _1$ norm of a beamformer output vector under the same distortionless constraint as that of the conventional minimum power distortionless response (MPDR) beamformer. Using the $\ell _1$ norm makes the beamformer output sparse. This leads to reducing the residual elements of the interference signal. In addition, the sensitivity of the proposed beamformer can be controlled by adding a norm constraint as in the MPDR beamformer. The proposed method improved the signal-to-interference-noise ratio by 7 dB from that of the MPDR beamformer for reverberation time $T_{60}$ = 300 ms in a simulation. [ABSTRACT FROM AUTHOR]

This paper proposes a versatile technique for integrating two conventional speech enhancement approaches, a spatial clustering approach (SCA) and a factorial model approach (FMA), which are based on two different features of signals, namely spatial and spectral features, respectively. When used separately the conventional approaches simply identify time frequency (TF) bins that are dominated by interference for speech enhancement. Integration of the two approaches makes identification more reliable, and allows us to estimate speech spectra more accurately even in highly nonstationary interference environments. This paper also proposes extensions of the FMA for further elaboration of the proposed technique, including one that uses spectral models based on mel-frequency cepstral coefficients and another to cope with mismatches, such as channel mismatches, between captured signals and the spectral models. Experiments using simulated and real recordings show that the proposed technique can effectively improve audible speech quality and the automatic speech recognition score. [ABSTRACT FROM AUTHOR]

We propose a new framework for joint multichannel speech source separation and acoustic noise reduction. In this framework, we start by formulating the minimum-mean-square error (MMSE)-based solution in the context of multiple simultaneous speakers and background noise, and outline the importance of the estimation of the activities of the speakers. The latter is accurately achieved by introducing a latent variable that takes N+1 possible discrete states for a mixture of N speech signals plus additive noise. Each state characterizes the dominance of one of the N+1 signals. We determine the posterior probability of this latent variable, and show how it plays a twofold role in the MMSE-based speech enhancement. First, it allows the extraction of the second order statistics of the noise and each of the speech signals from the noisy data. These statistics are needed to formulate the multichannel Wiener-based filters (including the minimum variance distortionless response). Second, it weighs the outputs of these linear filters to shape the spectral contents of the signals' estimates following the associated target speakers' activities. We use the spatial and spectral cues contained in the multichannel recordings of the sound mixtures to compute the posterior probability of this latent variable. The spatial cue is acquired by using the normalized observation vector whose distribution is well approximated by a Gaussian-mixture-like model, while the spectral cue can be captured by using a pre-trained Gaussian mixture model for the log-spectra of speech. The parameters of the investigated models and the speakers' activities (posterior probabilities of the different states of the latent variable) are estimated via expectation maximization. Experimental results including comparisons with the well-known independent component analysis and masking are provided to demonstrate the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimating the direction of arrival (DOA) of multiple source signals for an underdetermined situation, where the number of sources N exceeds the number of sensors M ( M < N). Some DOA estimation methods have already been proposed for underdetermined cases. However, since most of them restrict their microphone array arrangements, their DOA estimation ability is limited to a 2-dimensional plane. To deal with an underdetermined case where sources are distributed arbitrarily, we propose a method that can employ a 2- or 3-dimensional sensor array. Our new method employs the source sparseness assumption to handle an underdetermined case. Our formulation with the sensor coordinate vectors allows us to employ arbitrarily arranged sensors easily. We obtained promising experimental results for 2-dimensionally distributed sensors and sources 3×4, 3×5 (#sensors × #speech sources), and for 3-dimensional case with 4×5 in a room (reverberation time (RT) of 120 ms). We also investigate the DOA estimation performance under several reverberant conditions. [ABSTRACT FROM AUTHOR]

This paper presents a blind source separation method for convolutive mixtures of speech/audio sources. The method can even be applied to an underdetermined case where there are fewer microphones than sources. The separation operation is performed in the frequency domain and consists of two stages. In the first stage, frequency-domain mixture samples are clustered into each source by an expectation–maximization (EM) algorithm. Since the clustering is performed in a frequency bin-wise manner, the permutation ambiguities of the bin-wise clustered samples should be aligned. This is solved in the second stage by using the probability on how likely each sample belongs to the assigned class. This two-stage structure makes it possible to attain a good separation even under reverberant conditions. Experimental results for separating four speech signals with three microphones under reverberant conditions show the superiority of the new method over existing methods. We also report separation results for a benchmark data set and live recordings of speech mixtures. [ABSTRACT FROM PUBLISHER]

This paper proposes a microphone array-based speech activity detection (SAD) method for analyzing multi-party conversations recorded in the presence of noise. In particular, the proposed method considers conversations where the number of speakers and speaker locations cannot be restricted, such as when standing and talking, and at poster sessions. When we observe such conversations, there are directional noise sources and diffuse noise that affect the direction of arrival estimations of the target speech signals. To detect speech activity without a priori knowledge about the speakers and noise environments, a likelihood ratio test (LRT)-based SAD method is applied to spatial magnitude, which are estimated by using the time–frequency masking of the observed spectra. The proposed method can exploit the enhanced signals obtained from time–frequency masking, and works even in the presence of environmental noise. Experiments with recorded simulated poster sessions confirmed that the proposed method could outperform conventional methods based on the LRT for a single channel, magnitude coherence, or crosspower spectrum phase. [ABSTRACT FROM PUBLISHER]

In frequency-domain blind source separation (BSS) for speech with independent component analysis (ICA), a practical parametric Pearson distribution system is used to model distribution of frequency-domain source signals. ICA adaptation rules have a score function determined by an approximated distribution. Approximation based on the data may produce separation performance than we can obtain with ICA. Previously, conventional hyperbolic tangent (tanh) or generalized Gaussian distribution (GGD) was uniformly applied to the score function for all frequency bins, even though a wideband speech signal has different distributions at different frequencies. To deal with this, we propose modeling the signal distribution at each frequency by adopting a parametric Pearson distribution and employing to optimize the separation matrix in the ICA learning process. The score function is estimated by the appropriate Pearson distribution parameters for each frequency bin. We devised three methods for Pearson distribution parameter estimation and conducted separation experiments with real speech signals convolved with actual room impulse responses (T60 = 130 ms). Our experimental results show that the proposed frequency-domain Pearson-ICA (FD-Pearson-ICA) adapted well to the characteristics of frequency-domain source signals. By applying the FD-Pearson-ICA performance, the signal-to-interference ratio significantly improved by around 2-3 dB compared with conventional nonlinear functions. Even if the signal-to-interference ratio (SIR) values of FD-Pearson-lCA were poor, the performance based on a disparity measure between the true score function and estimated parametric score function clearly showed the advantage of FD-Pearson-lCA. Furthermore, we confirmed the optimum of the proposed approach for/optimized the proposed approach as regards separation performance. By combining individual distribution parameters directly estimated at low frequency with the appropriate parameters optimized at high frequency, it was possible to both reasonably improve the FD-Pearson-ICA performance without any significant increase in the computational burden by comparison with conventional nonlinear functions. [ABSTRACT FROM AUTHOR]

This paper proposes a new formulation and optimization procedure for grouping frequency components in frequency-domain blind source separation (BSS). We adopt two separation techniques, independent component analysis (ICA) and time-frequency (T-F) masking, for the frequency-domain BSS. With ICA, grouping the frequency components corresponds to aligning the permutation ambiguity of the ICA solution in each frequency bin. With T-F masking, grouping the frequency components corresponds to classifying sensor observations in the time-frequency domain for individual sources. The grouping procedure is based on estimating anechoic propagation model parameters by analyzing ICA results or sensor observations. More specifically, the time delays of arrival and attenuations from a source to all sensors are estimated for each source. The focus of this paper includes the applicability of the proposed procedure for a situation with wide sensor spacing where spatial aliasing may occur. Experimental results show that the proposed procedure effectively separates two or three sources with several sensor configurations in a real room, as long as the room reverberation is moderately low. [ABSTRACT FROM AUTHOR]

Acoustical signals are often corrupted by other speeches, sources, and background noise. This makes it necessary to use some form of preprocessing so that signal processing systems such as a speech recognizer or machine diagnosis can be effectively employed. In this contribution, we introduce and evaluate a new algorithm that uses independent component analysis (ICA) with a geometrical constraint [constrained ICA (CICA)]. It is based on the fundamental similarity between an adaptive beamformer and blind source separation with ICA, and does not suffer the permutation problem of ICA-algorithms. Unlike conventional ICA algorithms, CICA needs prior knowledge about the rough direction of the target signal. However, it is more robust against an erroneous estimation of the target direction than adaptive beamformers: CICA converges to the right solution as long as its look direction is closer to the target signal than to the jammer signal. A high degree of robustness is very important since the geometrical prior of an adaptive beamformer is always roughly estimated in a reverberant environment, even when the look direction is precise. The effectiveness and robustness of the new algorithms is proven theoretically, and shown experimentally for three sources and three microphones with several sets of real-world data. [ABSTRACT FROM AUTHOR]

This paper presents a method for enhancing target sources of interest and suppressing other interference sources. The target sources are assumed to be close to, sensors, to have dominant powers at these sensors, and to have non-Gaussianity. The enhancement is performed blindly, i.e., without knowing the position and active time of each source. We consider a general case where the total number of sources is larger than the number of sensors, and neither the number of target sources nor the total number of sources is known. The method is based on a two-stage process where independent component analysis (ICA) is first employed in each frequency bin and then time-frequency masking is used to improve the performance further. We propose a new sophisticated method for deciding the number of target sources and then selecting their frequency components. We also propose a new criterion for specifying time-frequency masks. Experimental results for simulated cocktail party situations in a room, whose reverberation time was 130 ms, are presented to show the effectiveness and characteristics of the proposed method. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) for convolutive mixtures can be solved efficiently in the frequency domain, where independent component analysis (ICA) is performed separately in each frequency bin. However, frequency-domain BSS involves a permutation problem: the permutation ambiguity of ICA in each frequency bin should be aligned so that a separated signal in the time-domain contains frequency components of the same source signal. This paper Presents a robust and precise method for solving the permutation problem. It is based on two approaches: direction of arrival (DOA) estimation for sources and the interfrequency correlation of signal envelopes. We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit their respective advantages. Furthermore, by utilizing the harmonics of signals, we make the new method robust even for low frequencies where DOA estimation is inaccurate. We also present a new closed-form formula for estimating DOAs from a separation matrix obtained by ICA. Experimental results show that our method provided an almost perfect solution to the permutation problem for a case where two sources were mixed in a room whose reverberation time was 300 ms. [ABSTRACT FROM AUTHOR]

Two types of inquiry were used to explore the norms governing the management of compliments in Japan and the United States. The first, a semistructured interview, revealed great differences in the frequency of complimenting behavior and in the hierarchy of attributes praised. Incidents from these interviews were used to construct a Complimentary Mode Questionnaire that was then administered to larger samples of Japanese and Americans. The resulting data indicated there were highly significant differences in the reported praising behavior of members of the two cultures. These differences appeared with regard to the status of communicative partners and with regard to the attributes admired. No sex differences were found. When the Japanese were expected to prefer more indirect forms of praise they also showed greater preference for many direct forms; when Americans were expected to prefer more direct forms of praise, they showed surprising preference for indirect forms. The relations of these patterns to other cultural variables and their implications for interaction between the two cultures are suggested. [ABSTRACT FROM PUBLISHER]