Independent Component Analysis.

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.

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A robust and precise method for solving the permutation problem of frequency-domain blind source separation

Blind source separation (BSS) is a challenging problem in real-world environments where sources are time delayed and convolved. The problem becomes more difficult in very reverberant conditions, with an increasing number of sources, and geometric configurations of the sources such that finding directionality is not sufficient for source separation. In this paper, we propose a new algorithm that exploits higher order frequency dependencies of source signals in order to separate them when they are mixed. In the frequency domain, this formulation assumes that dependencies exist between frequency bins instead of defining independence for each frequency bin. In this manner, we can avoid the well-known frequency permutation problem. To derive the learning algorithm, we define a cost function, which is an extension of mutual information between multivariate random variables. By introducing a source prior that models the inherent frequency dependencies, we obtain a simple form of a multivariate score function. In experiments, we generate simulated data with various kinds of sources in various environments. We evaluate the performances and compare it with other well-known algorithms. The results show the proposed algorithm outperforms the others in most cases. The algorithm is also able to accurately recover six sources with six microphones. In this case, we can obtain about 16-dB signal-to-interference ratio (SIR) improvement. Similar performance is observed in real conference room recordings with three human speakers reading sentences and one loudspeaker playing music

Blind Source Separation Exploiting Higher-Order Frequency Dependencies

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.

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The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech

In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks.

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A survey of convolutive blind source separation methods

This paper describes a new blind signal separation method using the directivity patterns of a microphone array. In this method, to deal with the arriving lags among each microphone, the inverses of the mixing matrices are calculated in the frequency domain so that the separated signals are mutually independent. Since the calculations are carried out in each frequency independently, the following problems arise: (1) permutation of each sound source, (2) arbitrariness of each source gain. In this paper, we propose a new solution that directivity patterns are explicitly used to estimate each sound source direction. As the results of signal separation experiments, it is shown that the proposed method improves the SNR of degraded speech by about 16 dB under non-reverberant condition. Also, the proposed method improves the SNR by 8.7 dB when the reverberation time is 184 ms, and by 5.1 dB when the reverberation time is 322 ms.

Evaluation of blind signal separation method using directivity pattern under reverberant conditions

Nowadays, e-learning systems are widely used for education and training in universities and companies because of their electronic course content access and virtual classroom participation However, with the rapid increase of learning content on the Web, it will be time-consuming for learners to find contents they really want to and need to study Aiming at enhancing the efficiency and effectiveness of learning, we propose an ontology-based approach for semantic content recommendation towards context-aware e-learning The recommender takes knowledge about the learner (user context), knowledge about content, and knowledge about the domain being learned into consideration Ontology is utilized to model and represent such kinds of knowledge The recommendation consists of four steps: semantic relevance calculation, recommendation refining, learning path generation, and recommendation augmentation As a result, a personalized, complete, and augmented learning program is suggested for the learner

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Ontology-based semantic recommendation for context-aware e-learning

The Center for Integrated Acoustic Information Research (CIAIR) at Nagoya University has been collecting speech corpora in moving cars which are made available as resources to advance the research and development of robust ASRs and spoken dialogue systems under high-noise conditions. The speech corpus consists of (1) phonetically balanced sentences, (2) digit strings, (3) discrete words and (4) transcribed spoken dialogues between drivers and information systems for navigation and information retrieval. These data are collected in vehicles under both idling and driving situations. The language of the corpus is currently Japanese. The number of subjects is currently about 300, total recording time is over 200 hours and total corpus size is about 160GByte. We have also been recording video images from three di erent angles, vehicle-control signals, and vehicle location, all synchronized with the speech recording. We report the objective of the speech corpus, the recording methods and the recording vehicle developed.

Construction of Speech Corpus in Moving Car Environment

This paper describes the interpolation of head related transfer functions (HRTFs) for all direction in the median plane. The interpolation of HRTFs enables us to reduce the number of measurements for new user's HRTFs, and also reduce the data of HRTFs in auditory virtual systems. In this paper, a simple linear interpolation method and the spline interpolation method are evaluated and advantages of both methods clarified. In experiments, the interpolation methods are applied to HRTFs measured using a dummy head. The experimental results show that the two methods are comparable in the best case. The resultant minimum spectral distortion is about 2 dB for both methods. The results clarify that the linear interpolation is effective for a set of elevations selected based on the cross correlation and that the spline interpolation is effective at large and equal intervals. These results indicate that HRTFs in the median plane can be interpolated by the methods.

Interpolating head related transfer functions in the median plane

This paper proposes space diversity speech recognition technique using distributed multi-microphones in a room, as a new paradigm of speech recognition. The key technology to realize the system is (1) distant-talking speech recognition and (2) the integration method of multiple inputs. In this paper, we propose the use of a distant speech model for distant-talking speech recognition, and feature-based and likelihood-based integration methods for multimicrophones distributed in the room. The distant speech model is a set of HMMs learned using speech data convolved with the impulse responses measured at several points in the room. The experimental results of simulated distant-talking speech recognition show that the proposed space diversity speech recognition system can attain about 80% in accuracy, while the performances of conventional HMMs using close-talking microphones are less than 50%. These results indicate that the space diversity approach is promising for robust speech recognition under a real acoustic environment.

Shoji Kajita

Papers

Evaluation of blind signal separation method using directivity pattern under reverberant conditions

Ontology-based semantic recommendation for context-aware e-learning

Construction of Speech Corpus in Moving Car Environment

Interpolating head related transfer functions in the median plane

Speech recognition based on space diversity using distributed multi-microphone