A self-calibrated speaker tracking system using both audio and video data

Hu, Jwu‐Sheng; Su, Tzung-Min; Cheng, Chih‐Chia; Liu, W.H.; Wu, Tsung-Hsiang

doi:10.1109/cca.2002.1038691

Cited by 8 publications

(3 citation statements)

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“…Assume a group of locations, , which are represented by the parameters with . The location is determined by finding the GM location model which has the maximum posteriori probability for a given observation sequences (11) where is a phase difference testing sequence derived from , and denotes the length of the testing sequence. If the probability densities at all locations are equally likely, then could be chosen as .…”

Section: Location Detectionmentioning

confidence: 99%

“…Additionally, for speech signal purification, a better receiving beam using a microphone array can be formed to suppress the environmental noises if the speaker's location is known. In a highly reflective or scattering environment, conventional delay estimation methods such as GCC-based algorithms [8]- [10] or previous works [11], [12] do not yield satisfactory results. Although Brandstein et al [13] proposed Tukey's Biweight to redefine the weighting function to deal with the reflection effect; it is not suitable for a noisy environment.…”

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confidence: 99%

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Robust speaker's location detection in a vehicle environment using GMM models

Cheng

Liu

2006

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Human-computer interaction (HCI) using speech communication is becoming increasingly important, especially in driving where safety is the primary concern. Knowing the speaker's location (i.e., speaker localization) not only improves the enhancement results of a corrupted signal, but also provides assistance to speaker identification. Since conventional speech localization algorithms suffer from the uncertainties of environmental complexity and noise, as well as from the microphone mismatch problem, they are frequently not robust in practice. Without a high reliability, the acceptance of speech-based HCI would never be realized. This work presents a novel speaker's location detection method and demonstrates high accuracy within a vehicle cabinet using a single linear microphone array. The proposed approach utilize Gaussian mixture models (GMM) to model the distributions of the phase differences among the microphones caused by the complex characteristic of room acoustic and microphone mismatch. The model can be applied both in near-field and far-field situations in a noisy environment. The individual Gaussian component of a GMM represents some general location-dependent but content and speaker-independent phase difference distributions. Moreover, the scheme performs well not only in nonline-of-sight cases, but also when the speakers are aligned toward the microphone array but at difference distances from it. This strong performance can be achieved by exploiting the fact that the phase difference distributions at different locations are distinguishable in the environment of a car. The experimental results also show that the proposed method outperforms the conventional multiple signal classification method (MUSIC) technique at various SNRs.

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Section: Location Detectionmentioning

confidence: 99%

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confidence: 99%

Robust speaker's location detection in a vehicle environment using GMM models

Cheng

Liu

2006

IEEE Trans. Syst., Man, Cybern. B

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“…The concept of employing a microphone array to localize sound source has been developed over 30 years [8][9][10][11][12][13][14][15]. However, most methods do not yield satisfactory results in highly reverberating, scattering or noisy environments, such as the phase correlation methods shown in [16].…”

Section: Introductionmentioning

confidence: 99%

A Robust Statistical-Based Speaker's Location Detection Algorithm in a Vehicular Environment

Cheng

Liu

2006

EURASIP J. Adv. Signal Process.

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This work presents a robust speaker's location detection algorithm using a single linear microphone array that is capable of detecting multiple speech sources under the assumption that there exist nonoverlapped speech segments among sources. Namely, the overlapped speech segments are treated as uncertainty and are not used for detection. The location detection algorithm is derived from a previous work (2006), where Gaussian mixture models (GMMs) are used to model location-dependent and content and speaker-independent phase difference distributions. The proposed algorithm is proven to be robust against the complex vehicular acoustics including noise, reverberation, near-filed, far-field, line-of-sight, and non-line-of-sight conditions, and microphones' mismatch. An adaptive system architecture is developed to adjust the Gaussian mixture (GM) location model to environmental noises. To deal with unmodeled speech sources as well as overlapped speech signals, a threshold adaptation scheme is proposed in this work. Experimental results demonstrate high detection accuracy in a noisy vehicular environment.

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Recent advances on multicue object tracking: a survey

Walia

Kapoor

2016

Artif Intell Rev

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A self-calibrated speaker tracking system using both audio and video data

Cited by 8 publications

References 11 publications

Robust speaker's location detection in a vehicle environment using GMM models

Robust speaker's location detection in a vehicle environment using GMM models

A Robust Statistical-Based Speaker's Location Detection Algorithm in a Vehicular Environment

Recent advances on multicue object tracking: a survey

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