• 한국정보통신학회논문지
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• 제4권5호
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• pp.1017-1025
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• 2000

인간의 청각시스템은 두 가지 요소 즉, ITD(Interaural Time Difference)와 IID(Interaural Intensity Difference)를 처리하여 음원의 위치와 추적을 하고 있다. 본 연구에서는 음원의 위치 검출을 위하여 ITD와 IID 뿐만 아니라 이전의 위치 정보를 이용하여 정확한 음원의 방향을 결정할 수 있는 TEPILD(Time Energy Previous Integration Location Detector) 모델을 제안하였다. TEPILD 모델에서 time function generator는 ITD, energy function generator는 IID를 처리할 수 있도록 하였다. 음원은 정현파(500Hz,1kHz, 2kHz, 3kHz), White noise, Pink noise, News, Music으로 하고 음원의 방향은 right, front right, front, front left, left로 하였다. 실험 결과 전체 평균 정확도가99.2로 좋은 결과를 얻을 수 있었으며, TEPILD가 음원 위치 검출기에 이용될 수 있음을 확인하였다.

• 말소리와 음성과학
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• 제2권3호
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• pp.141-148
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• 2010

In this paper, voice activity detection (VAD) for dual-channel noisy speech recognition is proposed in which spatial cues are employed. In the proposed method, a probability model for speech presence/absence is constructed using spatial cues obtained from dual-channel input signal, and a speech activity interval is detected through this probability model. In particular, spatial cues are composed of interaural time differences and interaural level differences of dual-channel speech signals, and the probability model for speech presence/absence is based on a Gaussian kernel density. In order to evaluate the performance of the proposed VAD method, speech recognition is performed for speech segments that only include speech intervals detected by the proposed VAD method. The performance of the proposed method is compared with those of several methods such as an SNR-based method, a direction of arrival (DOA) based method, and a phase vector based method. It is shown from the speech recognition experiments that the proposed method outperforms conventional methods by providing relative word error rates reductions of 11.68%, 41.92%, and 10.15% compared with SNR-based, DOA-based, and phase vector based method, respectively.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.289-290
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• 2007

In order to improve the performance of a speech recognition system in the presence of noise, we propose a noise robust front-end using two-channel speech signals by separating speech from noise based on the computational auditory scene analysis (CASA). The main cues for the separation are interaural time difference (ITD) and interaural level difference (ILD) between two-channel signal. As a result, we can extract 39 cepstral coefficients are extracted from separated speech components. It is shown from speech recognition experiments that proposed front-end has outperforms the ETSI front-end with single-channel speech.

• 대한음성학회지:말소리
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• 제57호
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• pp.85-97
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• 2006

This paper suggests an algorithm that can estimate the direction of the sound source in real time. The algorithm uses the time difference and sound intensity information among the recorded sound source by four microphones. Also, to deal with noise of robot itself, the Kalman filter is implemented. The proposed method can take shorter execution time than that of an existing algorithm to fit the real-time service robot. Also, using the Kalman filter, signal ratio relative to background noise, SNR, is approximately improved to 8 dB. And the estimation result of azimuth shows relatively small error within the range of ${\pm}7$ degree.