• Title, Summary, Keyword: Speech Enhancement

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An Adaptive Wind Noise Reduction Method Based on a priori SNR Estimation for Speech Eenhancement (음성 강화를 위한 a priori SNR 추정기반 적응 바람소리 저감 방법)

  • Seo, Ji-Hun;Lee, Seok-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.12
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    • pp.1756-1760
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    • 2015
  • This paper focuses on a priori signal to noise ratio (SNR) estimation method for the speech enhancement. There are many researches for speech enhancement with several ambient noise cancellation methods. The method based on spectral subtraction (SS) which is widely used in noise reduction has a trade-off between the performance and the distortion of the signals. So the need of adaptive method like an estimated a priori SNR being able to making a high performance and low distortion is increasing. The decision directed (DD) approach is used to determine a priori SNR in noisy speech signals. A priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a modified a priori SNR estimator and the weighted rational transfer function for speech enhancement with wind noises. The experimental result shows the performance of our proposed estimator is better Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862) compare to the conventional DD approach-based systems and different noise reduction methods.

Speech Enhancement using RNN Phoneme based VAD (음소기반의 순환 신경망 음성 검출기를 이용한 음성 향상)

  • Lee, Kang;Kang, Sang-Ick;Kwon, Jang-woo;Lee, Samgmin
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.5
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    • pp.85-89
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    • 2017
  • In this papers, we apply high performance hardware and machine learning algorithm to build an advanced VAD algorithm for speech enhancement. Since speech is made of series of phoneme, using recurrent neural network (RNN) which consider previous data is proper method to build a speech model. It is impossible to study every noise in real world. So our algorithm is builded by phoneme based study. we detect voice present frames in noisy speech signal and make enhancement of the speech signal. Phoneme based RNN model shows advanced performance in speech signal which has high correlation among each frames. To verify the performance of proposed algorithm, we compare VAD result with label data and speech enhancement result in various noise environments with previous speech enhancement algorithm.

Multi-Channel Speech Enhancement Algorithm Using DOA-based Learning Rate Control (DOA 기반 학습률 조절을 이용한 다채널 음성개선 알고리즘)

  • Kim, Su-Hwan;Lee, Young-Jae;Kim, Young-Il;Jeong, Sang-Bae
    • Phonetics and Speech Sciences
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    • v.3 no.3
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    • pp.91-98
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    • 2011
  • In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

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A Single Channel Speech Enhancement for Automatic Speech Recognition

  • Lee, Jinkyu;Seo, Hyunson;Kang, Hong-Goo
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.85-88
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    • 2011
  • This paper describes a single channel speech enhancement as the pre-processor of automatic speech recognition system. The improvements are based on using optimally modified log-spectra (OM-LSA) gain function with a non-causal a priori signal-to-noise ratio (SNR) estimation. Experimental results show that the proposed method gives better perceptual evaluation of speech quality score (PESQ) and lower log-spectral distance, and also better word accuracy. In the enhancement system, parameters was turned for automatic speech recognition.

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A study of speech. enhancement through wavelet analysis using auditory mechanism (인간의 청각 메커니즘을 적용한 웨이블렛 분석을 통한 음성 향상에 대한 연구)

  • 이준석;길세기;홍준표;홍승홍
    • Proceedings of the IEEK Conference
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    • pp.397-400
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    • 2002
  • This paper has been studied speech enhancement method in noisy environment. By mean of that we prefer human auditory mechanism which is perfect system and applied wavelet transform. Multi-resolution of wavelet transform make possible multiband spectrum analysis like human ears. This method was verified very effective way in noisy speech enhancement.

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Subspace Speech Enhancement Using Subband Whitening Filter (서브밴드 백색화 필터를 이용한 부공간 잡음 제거)

  • 김종욱;유창동
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.3
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    • pp.169-174
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    • 2003
  • A novel subspace speech enhancement using subband whitening filter is proposed. Previous subspace speech enhancement method either assumes additive white noise or uses whitening filter as a pre-processing for colored noise. The proposed method tries to minimize the signal distortion while reducing residual noise by processing the signal using subband whitening filter. By incorporating the notion of subband whitening filter, spectral resolution in Karhunen-Loeve(KL) domain is improved with the negligible additional computational load. The proposed method outperforms both the subspace method suggested by Ephraim and the spectral subtraction suggested by Boll in terms of segmental signal-to-noise ratio (SNRseg) and perceptual evaluation of speech quality (PESQ).

Speech Enhancement Based on Psychoacoustic Model (심리음향모델에 근거한 음성개선)

  • Lee Jingeol
    • Proceedings of the Acoustical Society of Korea Conference
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    • pp.337-338
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    • 2000
  • The perceptual filter for speech enhancement was analytically derived where the frequency content of the input noisy signal was made the same as that of the estimated clean signal in auditory domain. However, the analytical derivation should rely on the deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In order to cope with the problem associated with the deconvolution, we propose a novel psychoacoustic model based speech enhancement filter whose principle is the same as the perceptual filter, however the filter is derived by a constrained optimization which provides solutions to the ill-conditioned problem.

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Speech enhancement using psychoacoustics model (사이코어쿠스틱스 모델을 이용한 음성 향상)

  • Kwon, Chul-Hyun;Shin, Dae-Kyu;Park, Sang-Hui
    • Proceedings of the KIEE Conference
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    • pp.748-750
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    • 1999
  • In this study, a speech enhancement is presented based on the utilization of well-known auditory mechanism, noise masking. The speech enhancement approach adopted here is to derive an modifier that achieves audible noise suppression. This modification selectively affects the perceptually significant spectral values, and is therefore less prone to introduction of unwanted distortions than methods that affect the complete STSA and produces more enhanced results at low SNR as well as at high SNR. The speech enhancement method adopted here needs exact estimation of the minimum specteal value per critical band because it uses only the minimum spectral value per critical band. For this, the method adopted here uses the modified spectral subtraction that is more flexible than power spectral subtraction. So, the result in experiment represented better SNR than before.

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Filtering of a Dissonant Frequency Combined with Noise Reduction for Speech Enhancement (잡음 감소와 불협화음 제거를 통한 음성신호 향상)

  • Sangki Kang;Lee, Youn-Jeong;Lee, Ki-Yong
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.1E
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    • pp.16-18
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    • 2004
  • There have been numerous studies on the enhancement of the noisy speech signal. In this paper, I propose a completely new speech enhancement method, that is, a filtering of a dissonant frequency combined with noise reduction algorithm. The simulation results indicate that the proposed method provides a significant gain in audible improvement compared with the conventional method. Therefore if the proposed enhancement scheme is used as a pre-filter, the perceptual quality of speech is greatly enhanced.

Noise Reduction Using the Standard Deviation of the Time-Frequency Bin and Modified Gain Function for Speech Enhancement in Stationary and Nonstationary Noisy Environments

  • Lee, Soo-Jeong;Kim, Soon-Hyob
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.3E
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    • pp.87-96
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    • 2007
  • In this paper we propose a new noise reduction algorithm for stationary and nonstationary noisy environments. Our algorithm classifies the speech and noise signal contributions in time-frequency bins, and is not based on a spectral algorithm or a minimum statistics approach. It relies on calculating the ratio of the standard deviation of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. We show that good quality can be achieved for enhancement speech signal by choosing appropriate values for ${\delta}_t\;and\;{\delta}_f$. The proposed method greatly reduces the noise while providing enhanced speech with lower residual noise and somewhat higher mean opinion score (MOS), background intrusiveness (BAK) and signal distortion (SIG) scores than conventional methods.