• 음성과학
• /
• 제4권2호
• /
• pp.5-15
• /
• 1998

This paper proposes a speech state-dependent spectral subtraction method to regulate the blind spectral subtraction for improved enhancement. In the proposed method, a modified subtraction rule is applied over the speech selectively contingent to the speech state being voiced or unvoiced, in an effort to incorporate the acoustic characteristics of phonemes. In particular, the objective of the proposed method is to remedy the subtraction induced signal distortion attained by two state-dependent procedures, spectrum sharpening and minimum spectral bound. In order to remove the residual noise, the proposed method employs a procedure utilizing the masking effect. Proposed spectral subtraction including state-dependent subtraction and residual noise reduction using the masking threshold shows effectiveness in compensation of spectral distortion in the unvoiced region and residual noise reduction.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1998년도 하계종합학술대회논문집
• /
• pp.599-602
• /
• 1998

In this paper, a speech enhancement method based on phonemic properties and masking effect is propsoed. It is a modified type of spectral subtraction wherein the spectral sharpening process is exploited in unvoiced state considering the phonemic properties. The masking threshold is used to remove the residual noise. The proposed spectral subtraction shows similar performance as that of the classical spectral subtraction method in view of the SNR. But by the prposed scheme, the unvoiced sound region is shown to exhibit relatively less signal distortion in the enhanced speech.

• Journal of Information Processing Systems
• /
• 제15권4호
• /
• pp.748-764
• /
• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

• 한국음향학회지
• /
• 제18권1호
• /
• pp.38-44
• /
• 1999

본 연구에서는 단일 채널 단구간 진폭 스펙트럼 추정 기법의 하나인 Spectral Subtraction 방법과 2 채널 Griffiths-Jim Beamformer를 결합한 음성 강조기법을 제안한다. 기존의 단구간 진폭 스펙트럼 추정 기법에서는 관측된 신호의 스펙트럼에서 잡음의 평균 스펙트럼을 감산하여 잡음을 제거하고 있지만, 이 방법을 이용하여 잡음을 제거 할 경우에는 잡음 변동시 잡음 억제 능력이 미약하고, 목적 신호의 단구간 진폭 스펙트럼 추정 성능이 낮아진다는 단점을 갖고 있다. 그 이유는 실제 잡음의 스펙트럼은 평균값 주위에 분산되어 있기 때문이 다. 그러므로, 2 채널 Beamformer의 사각(Blocking Matrix)를 이용하여 분석 구간에서의 잡음의 단구간 진폭 스펙트럼을 추정하고, 이 추정된 값을 이용하여 목적 신호의 스펙트럼을 추정하는 기법을 제안하고, 컴퓨터 시뮬레이션을 통하여 그 유효성을 입증한다.

• The Journal of the Acoustical Society of Korea
• /
• 제22권2E호
• /
• pp.62-68
• /
• 2003

This paper addresses a novel noise-compensation scheme to solve the mismatch problem between training and testing condition for the automatic speech recognition (ASR) system, specifically in car environment. The conventional spectral subtraction schemes rely on the signal-to-noise ratio (SNR) such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, these schemes are based on the postulation that the power spectrum of noise is in general at the lower level in magnitude than that of speech. Therefore, while such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as that of car environment. This paper proposes an efficient spectral subtraction scheme focused specifically to low SNR noisy environment by extracting harmonics distinctively in speech spectrum. Representative experiments confirm the superior performance of the proposed method over conventional methods. The experiments are conducted using car noise-corrupted utterances of Aurora2 corpus.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2003년도 5월 학술대회지
• /
• pp.207-210
• /
• 2003

In this paper, the combinations of speech enhancement techniques are experimented. Specifically, the spectral subtraction, KLT based comb-filtering, and their combinations are applied to the Aurora2 database. The results show that recognition accuracy is improved when KLT based comb-filtering is applied after spectral subtraction.

• The Journal of the Acoustical Society of Korea
• /
• 제23권4E호
• /
• pp.125-133
• /
• 2004

In this paper, we propose a critical banded wavelet packet-based spectral subtraction for speech enhancement. Critical banded wavelet packet, which reflects the human auditory system, may lead to minimization of intelligibility loss and quality improvement of the enhanced speech in the spectral domain, when combined with an appropriate spectral subtraction gain function. The proposed method shows better performance than the conventional one in comparative assessments. We also show that, for effective evaluation of enhanced speech, it is essential to consider the characteristics of speech quality measures.

• 한국정보통신학회논문지
• /
• 제13권10호
• /
• pp.2211-2217
• /
• 2009

본 논문에서는 잔향이 존재하는 환경에서 단일 마이크로폰을 사용한 음성 개선 방법을 제시한다. 스펙트럼 차감법(Spectral Subtraction)은 스펙트럼 상에서 잔향성분 및 잡음을 제거 할 수 있는 효과적인 방법이다. 스펙트럼 차감법은 음성과 비음성 구간의 정확한 구분을 필요로 하며 성능을 향상시키기 위해 본 논문에서는 엔트로피(Entropy) 기반의 음성 구간 검출법을 적용하였다. 제시된 방법을 기존의 에너지 검출 기반의 음성 검출법을 적용한 스펙트럼 차감법과 비교하여 성능 평가를 수행하였다. SNR 및 잔향시간에 따른 잔향 제거비율을 평가지표로 사용하였으며, 시뮬레이션 결과 기존의 스펙트럼 차감법과 비교하여 제시된 방법이 우수한 성능을 보였다.

• The Journal of the Acoustical Society of Korea
• /
• 제22권2E호
• /
• pp.87-92
• /
• 2003

In this paper, a new speech enhancement method using level adapted wavelet packet is presented. First, we propose a level adapted wavelet packet to alleviate a drawback of the conventional node adapted one in noisy environment. Next, we suggest an adaptive noise estimation method at each node on level adapted wavelet packet tree. Then, for more accurate noise component subtraction, we propose a new estimation method of spectral subtraction weight. Finally, we present a modified spectral subtraction method. The proposed method is evaluated on various noise conditions: speech babble noise, F-l6 cockpit noise, factory noise, pink noise, and Volvo car interior noise. For an objective evaluation, the SNR test was performed. Also, spectrogram test and a very simple listening test as a subjective evaluation were performed.

• 한국정보통신학회논문지
• /
• 제14권4호
• /
• pp.991-998
• /
• 2010

실내 환경에서 음성인식 기술을 이용한 무선 홈 네트워크 시스템 구현에 있어, 잡음과 실내 잔향음은 시스템 성능 저하의 주요 원인이다. 본 연구에서는 실내 인식환경에서 스펙트럼 엔트로피(Spectral entropy) 기반의 음성 구간검출법을 이용하여 잔향음(reverberation) 및 실내잡음에 강인한 음성인식 홈 네트워크 시스템을 구현하고자 한다. 스펙트럼 차감법(Spectral Subtraction)은 잔향으로 인해 왜곡된 신호를 스펙트럼 상에서 제거하여 잔향의 효과를 줄일 수 있고 음성신호와 독립적인 잡음을 제거 할 수 있다. 효과적인 스펙트럼 차감을 위해서는 음성과 비음성 구간의 정확한 구분이 수반되어야 하며 이를 위해서 엔트로피 기반의 음성 구간 검출법을 적용하여 성능을 향상시킨다. 모의 및 실내환경 실험 결과 Spectral entropy 기반의 음성 구간 검출법을 이용할 경우 실내 잔향 및 잡음환경에서 명령어 인식률의 향상이 증명되었다.