In this paper, we describe an approach to accelerate fingerprint techniques by skipping the search for irrelevant sections of the signal and demonstrate its application to the divide and locate (DAL) audio fingerprint method. The search result for the applied method, DAL3, is the same as that of DAL mathematically. Experimental results show that DAL3 can reduce the computational cost of DAL to approximately 25% for the task of music signal retrieval.

This paper discusses a new extension of hidden Markov models that can capture clusters embedded in transitions between the hidden states. In our model, the state-transition matrices are viewed as representations of relational data reflecting a network structure between the hidden states. We specifically present a nonparametric Bayesian approach to the proposed state-space model whose network structure is represented by a Mondrian Process-based relational model. We show an application of the proposed model to music signal analysis through some experimental results.

This paper deals with a novel concept of an exponential IDF in the BM25 formulation and compares the search accuracy with that of the BM25 with the original IDF in a content-based video retrieval (CBVR) task. Our video retrieval method is based on a bag of keypoints (local visual features) and the exponential IDF estimates the keypoint importance weights more accurately than the original IDF. The exponential IDF is capable of suppressing the keypoints from frequently occurring background objects in videos, and we found that this effect is essential for achieving improved search accuracy in CBVR. Our proposed method is especially designed to tackle instance video search, one of the CBVR tasks, and we demonstrate its effectiveness in significantly enhancing the instance search accuracy using the TRECVID2012 video retrieval dataset.

This paper proposes a novel representation of music that can be used for similarity-based music information retrieval, and also presents a method that converts an input polyphonic audio signal to the proposed representation. The representation involves a 2-dimensional tree structure, where each node encodes the musical note and the dimensions correspond to the time and simultaneous multiple notes, respectively. Since the temporal structure and the synchrony of simultaneous events are both essential in music, our representation reflects them explicitly. In the conventional approaches to music representation from audio, note extraction is usually performed prior to structure analysis, but accurate note extraction has been a difficult task. In the proposed method, note extraction and structure estimation is performed simultaneously and thus the optimal solution is obtained with a unified inference procedure. That is, we propose an extended 2-dimensional infinite probabilistic context-free grammar and a sparse factor model for spectrogram analysis. An efficient inference algorithm, based on Markov chain Monte Carlo sampling and dynamic programming, is presented. The experimental results show the effectiveness of the proposed approach.

We propose a music segment detection method for audio signals. Unlike many existing methods, ours specifically focuses on a background-music detection task, that is, detecting music used in background of main sounds. This task is important because music is almost always overlapped by speech or other environmental sounds in visual materials such as TV programs. Our method consists of feature extraction, dimension reduction, and statistical discrimination steps. For each step, we analyzed a set of methods to maximize the detection accuracy. With a simple post processing step, we achieved a framewise error rate as low as 8 % even when the mixed speech was louder than the target music by 10dB.

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.

This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M60= 120 ms)

This paper describes the frequency-domain blind source separation (BSS) of convolutively mixed acoustic signals using independent component analysis (ICA). The most critical issue related to frequency domain BSS is the permutation problem. This paper presents two methods for solving this problem. Both methods are based on the clustering of information derived from a separation matrix obtained by ICA. The first method is based on direction of arrival (DOA) clustering. This approach is intuitive and easy to understand. The second method is based on normalized basis vector clustering. This method is less intuitive than the DOA based method, but it has several advantages. First, it does not need sensor array geometry information. Secondly, it can fully utilize the information contained in the separation matrix, since the clustering is performed in high-dimensional space. Experimental results show that our methods realize BSS in various situations such as the separation of many speech signals located in a 3-dimensional space, and the extraction of primary sound sources surrounded by many background interferences

This paper describes a method for solving the permutation problem of frequency-domain blind source separation (BSS). The method analyzes the mixing system information estimated with independent component analysis (ICA). When we use widely spaced sensors or increase the sampling rate, spatial aliasing may occur for high frequencies due to the possibility of multiple cycles in the sensor spacing. In such cases, the estimated information would imply multiple possibilities for a source location. This causes some difficulty when analyzing the information. We propose a new method designed to overcome this difficulty. This method first estimates the model parameters for the mixing system at low frequencies where spatial aliasing does not occur, and then refines the estimations by using data at all frequencies. This refinement leads to precise parameter estimation and therefore precise permutation alignment. Experimental results show the effectiveness of the new method

We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the observation vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with non-linear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 times 4, 4 times 5 (#sensors times #sources) in a room (RT60= 120 ms)

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

Musical noise is a typical problem with blind source separation using a time-frequency mask. We report that a fine-shift and overlap-add method reduces the musical noise without degrading the separation performance. The effectiveness was confirmed by results of a listening test undertaken in a room with a reverberation time of RT/sub 60/=130 ms.

This paper presents a method for enhancing a dominant target source that is close to sensors, and suppressing other interferences. The enhancement is performed blindly, i.e. without knowing the number of total sources or information about each source, such as position and active time. We consider a general case where the number of sources is larger than the number of sensors. We employ a two-stage processing technique where a spatial filter is first employed in each frequency bin and time-frequency masking is then used to improve the performance further. To obtain the spatial filter we employ independent component analysis and then select the component of the target source. Time-frequency masks in the second stage are obtained by calculating the angle between the basis vector corresponding to the target source and a sample vector. The experimental results for a simulated cocktail party situation were very encouraging.

In this paper, we propose a method for separating speech signals when there are more signals than sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks to extract the signals, and therefore, their extracted signals contain loud musical noise. To overcome this problem, we propose combining a sparseness approach and independent component analysis (ICA). First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source from the observations and apply ICA to the remaining mixtures. Experimental results show that our proposed sparseness and ICA (SPICA) method can separate signals with little distortion even in reverberant conditions of T/sub R/=130 and 200 ms.

The paper presents a method for solving the permutation problem of frequency domain blind source separation (BSS) when source signals come from the same or similar directions. Geometric information such as the direction of arrival (DOA) is helpful for solving the permutation problem, and a combination of the DOA based and correlation based methods provides a robust and precise solution. However, when signals come from similar directions, the DOA based approach fails, and we have to use only the correlation based method whose performance is unstable. We show that an interpretation of the ICA solution by a near-field model yields information about spheres on which source signals exist, which can be used as an alternative to the DOA. Experimental results show that the proposed method can robustly separate a mixture of signals arriving from the same direction.

Blind source separation (BSS) for convolutive mixtures can be efficiently achieved in the frequency domain, where independent component analysis is performed separately in each frequency bin. However, frequency-domain BSS involves a permutation problem, which is well known as a difficult problem, especially when the number of sources is large. This paper presents a method for solving the permutation problem, which works well even for many sources. The successful solution for the permutation problem highlights another problem with frequency-domain BSS that arises from the circularity of the discrete frequency representation. This paper discusses the phenomena of the problem and presents a method for solving it. With these two methods, we can separate many sources with a practical execution time. Moreover, real-time processing is currently possible for up to three sources with our implementation.

In this paper, single-input multiple-output (SIMO)-model-based blind source separation (BSS) is addressed, where unknown mixed source signals are detected at the microphones, and these signals can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of the SIMO-model-based BSS methods, namely, traditional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance in comparison to two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of FDICA-PB, but the proposed combination technique can outperform both simple FDICA-PB and SIMO-ICA.

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.

This paper describes a robust real-time blind source separation (BSS) method for moving speech signals in a room. Our method employs frequency domain independent component analysis (ICA) using a blockwise batch algorithm in the first stage, and the separated signals are refined by postprocessing using crosstalk component estimation and nonstationary spectral subtraction in the second stage. The blockwise batch algorithm achieves better performance than an online algorithm when sources are fixed, and the postprocessing compensates for performance degradation caused by source movement. Experimental results using speech signals recorded in a real room show that the proposed method realizes robust real-time separation for moving sources. Our method is implemented on a standard PC and works in real time.

This paper presents a robust and precise method for solving the permutation problem of frequency-domain blind source separation. It is based on two previous approaches: the direction of arrival estimation approach and the inter-frequency correlation approach. We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit the both advantages. We also present a closed form formula to calculate a null direction, which is used in estimating the directions of source signals. Experimental results show that our method solved permutation problems almost perfectly for a situation that two sources were mixed in a room whose reverberation time was 300 ms.

Despite several recent proposals to achieve blind source separation (BSS) for realistic acoustic signals, the separation performance is still not good enough. In particular, when the impulse responses are long, performance is highly limited. In this paper, we consider a two-input, two-output convolutive BSS problem. First, we show that it is not good to be constrained by the condition T>P, where T is the frame length of the DFT and P is the length of the room impulse responses. We show that there is an optimum frame size that is determined by the trade-off between maintaining the number of samples in each frequency bin to estimate statistics and covering the whole reverberation. We also clarify the reason for the poor performance of BSS in long reverberant environments, highlighting that the framework of BSS works as two sets of frequency-domain adaptive beamformers. Although BSS can reduce reverberant sounds to some extent like adaptive beamformers, they mainly remove the sounds from the jammer direction. This is the reason for the difficulty of BSS in reverberant environments.

Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., Adaptive Beamformers (ABFs). The minimization of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABF. The unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense if the two source signals are ideally independent. Therefore, the performance of the BSS is limited by that of the ABF. This understanding. gives an interpretation of BSS from physical point of view.

This paper describes a post processing method to refine output signals obtained by Blind Source Separation (BSS). The performance of BSS using Independent Component Analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the cross-talk components derived from the reverberation of the jammer signal. Utilizing this knowledge, we propose a new method, time-delayed non-stationary spectral subtraction, which removes the residual components from the separated signals precisely. The proposed method compensates for the weakness of BSS in a reverberant environment. Experimental results using speech signals show that the proposed method improves the signal-to-noise ratio by 3 to 5 dB.

This paper presents a new type of nonlinear function for independent component analysis to process complex-valued signals, which is used in frequency-domain blind source separation. The new function is based on the polar coordinates of a complex number, whereas the conventional one is based on the Cartesian coordinates. The new function is derived from the probability density function of frequency-domain signals that are assumed to be independent of the phase. We show that the difference between the two types of functions is in the assumed densities of independent components. Experimental results for separating speech signals show that the new nonlinear function behaves better than the conventional one.

Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, that is, Adaptive Null Beamformers (ANB). The unmixing matrix of the BSS and the filter coefficients of the ANB converge to the same solution in the mean square error sense if the two source signals are ideally independent. This understanding clearly explains the poor performance of the BSS in a real room with long reverberation. The fundamental difference exists in the adaptation period when they should adapt. That is, the ANB can adapt in the presence of a jammer but the absence of a target, whereas the BSS can adapt in the presence of a target and jammer, and also in the presence of only a target.

In this paper, we investigate the separation and dereverberation performance of frequency domain Blind Source Separation (BSS) based on Independent Component Analysis (ICA) by measuring impulse responses of a system. Since ICA is a statistical method, i.e., it only attempts to make outputs independent, it is not easy to predict what is going on in a BSS system physically. We therefore investigate the detailed components in the processed signals of a whole BSS system from a physical and acoustical viewpoint. In particular, we focus on the direct sound and reverberation in the target and jammer signals. As a result, we reveal that the direct sound of a jammer can be removed and the reverberation of the jammer can be reduced to some degree by BSS, while the reverberation of the target cannot be reduced. Moreover, we show that a long frame length causes pre-echo noise, and this damages the quality of the separated signal. 1.