HMM-based separation of acoustic transfer function for single-channel sound source localization

Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.1109/icassp.2010.5496188

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…In our previous work, 12 using the estimated frame sequence data of the acoustic transfer function, the GMM for the acoustic transfer function was trained for each user's position. For test data, the talker position was estimated by finding a GMM having the maximum-likelihood from among the estimated GMMs corresponding to each position.…”

Section: Dimensional Feature Weighting and Classification Using mentioning

confidence: 99%

“…12 The estimation is implemented by maximizing the likelihood of the observed speech data from a user's position.…”

Section: B Maximum-likelihood-based Parameter Estimationmentioning

confidence: 99%

“…In our previous work, 12 we discussed a method for discriminating the location of the sound source using only a single microphone. In that report, the acoustic transfer function was estimated from observed (reverberant) speech using a clean speech model without texts of the user's utterances, and a hidden Markov model (HMM) was used to model the features of the clean speech.…”

Section: Introductionmentioning

confidence: 99%

“…Using HMM separation, 12 it is possible to estimate the acoustic transfer function using some adaptation data (only several words) uttered from a given position. For this reason, measurement of impulse responses is not required.…”

Section: Introductionmentioning

confidence: 99%

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Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function

Takashima

Takiguchi

Ariki

2013

The Journal of the Acoustical Society of America

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This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

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Section: Dimensional Feature Weighting and Classification Using mentioning

confidence: 99%

“…12 The estimation is implemented by maximizing the likelihood of the observed speech data from a user's position.…”

Section: B Maximum-likelihood-based Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function

Takashima

Takiguchi

Ariki

2013

The Journal of the Acoustical Society of America

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“…Takashima, et al [23] use an HMM-based approach to separate the acoustic transfer function so that they can separate the sources, using a single microphone. It is done by using an HMM model of the speech of each speaker to estimate the acoustic transfer function from each position in the room.…”

Section: Multiple Sources Of Speech and Far-field Audio Capturementioning

confidence: 99%

Speaker Recognition: Advancements and Challenges

Beigi¹

2012

New Trends and Developments in Biometrics

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Speaker Recognition is a multi-disciplinary branch of biometrics that may be used for identification, verification, and classification of individual speakers, with the capability of tracking, detection, and segmentation by extension. Recently, a comprehensive book on all aspects of speaker recognition was published [1]. Therefore, here we are not concerned with details of the standard modeling which is and has been used for the recognition task. In contrast, we present a review of the most recent literature and briefly visit the latest techniques which are being deployed in the various branches of this technology.Most of the works being reviewed here have been published in the last two years. Some of the topics, such as alternative features and modeling techniques, are general and apply to all branches of speaker recognition. Some of these general techniques, such as whispered speech, are related to the advanced treatment of special forms of audio which have not received ample attention in the past. Finally, we will follow by a look at advancements which apply to specific branches of speaker recognition [1], such as verification, identification, classification, and diarization. This chapter is meant to complement the summary of speaker recognition, presented in [2], which provided an overview of the subject. It is also intended as an update on the methods described in [1]. In the next section, for the sake of completeness, a brief history of speaker recognition is presented, followed by sections on specific progress as stated above, for globally applicable treatment and methods, as well as techniques which are related to specific branches of speaker recognition. A brief historyThe topic of speaker recognition [1] has been under development since the mid-twentieth century. The earliest known papers on the subject, published in the 1950s [3,4], were in search of finding personal traits of the speakers, by analyzing their speech, with some statistical underpinning. With the advent of early communication networks, Pollack, et al. [3] noted the need for speaker identification. Although, they employed human listeners to do the identification of individuals and studied the importance of the duration of speech and other facets that help in the recognition of a speaker. In most of the early

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