마이크로폰 배열에서 독립벡터분석 기법을 이용한 잡음음성의 음질 개선
Microphone Array Based Speech Enhancement Using Independent Vector Analysis
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참고문헌
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초록
Speech enhancement aims to improve speech quality by removing background noise from noisy speech. Independent vector analysis is a type of frequency-domain independent component analysis method that is known to be free from the frequency bin permutation problem in the process of blind source separation from multi-channel inputs. This paper proposed a new method of microphone array based speech enhancement that combines independent vector analysis and beamforming techniques. Independent vector analysis is used to separate speech and noise components from multi-channel noisy speech, and delay-sum beamforming is used to determine the enhanced speech among the separated signals. To verify the effectiveness of the proposed method, experiments for computer simulated multi-channel noisy speech with various signal-to-noise ratios were carried out, and both PESQ and output signal-to-noise ratio were obtained as objective speech quality measures. Experimental results have shown that the proposed method is superior to the conventional microphone array based noise removal approach like GSC beamforming in the speech enhancement.
Abstract
Speech enhancement aims to improve speech quality by removing background noise from noisy speech. Independent vector analysis is a type of frequency-domain independent component analysis method that is known to be free from the frequency bin permutation problem in the process of blind source separation from multi-channel inputs. This paper proposed a new method of microphone array based speech enhancement that combines independent vector analysis and beamforming techniques. Independent vector analysis is used to separate speech and noise components from multi-channel noisy speech, and delay-sum beamforming is used to determine the enhanced speech among the separated signals. To verify the effectiveness of the proposed method, experiments for computer simulated multi-channel noisy speech with various signal-to-noise ratios were carried out, and both PESQ and output signal-to-noise ratio were obtained as objective speech quality measures. Experimental results have shown that the proposed method is superior to the conventional microphone array based noise removal approach like GSC beamforming in the speech enhancement.
참고문헌 (9)
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- Benesty, J., "Microphone array signal processing" (2008), Springer Verlag
- Hyvarinen, A., "Independentcomponent analysis" (2001), Wiley
- Haykin, S., "Neural networks and learning machines" (2009), Pearson Prentice Hall
- Asano, F., "A combined approach of array processing andindependent component analysis for blind separation of acousticsignals", in Proc.IEEE Int. Conf. Acoust., Speech SignalProcess, pp.2729~2732 (2001)
- Kim, T., "Blind sourceseparation exploiting higher-order frequency dependencies,Audio, Speech, and Language Processing", IEEE Transactionson, 15, pp.70~79 (2007)
- "PESQ"[URL]
- "Systems Array Processing Toolbox"[URL]
- "NoiseX-92"[URL]