• Title/Summary/Keyword: recursive AMDF

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Implementation of the single channel adaptive noise canceller using TMS320C30 (TMS320C30을 이용한 단일채널 적응잡음제거기 구현)

  • Jung, Sung-Yun;Woo, Se-Jeong;Son, Chang-Hee;Bae, Keun-Sung
    • Speech Sciences
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    • v.8 no.2
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    • pp.73-81
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    • 2001
  • In this paper, we focus on the real time implementation of the single channel adaptive noise canceller(ANC) by using TMS320C30 EVM board. The implemented single channel adaptive noise canceller is based on a reference paper [1] in which it is simulated by using the recursive average magnitude difference function(AMDF) to get a properly delayed input speech on a sample basis as a reference signal and normalized least mean square(NLMS) algorithm. To certify results of the real time implementation, we measured the processing time of the ANC and enhancement ratio according to various signalto-noise ratios(SNRs). Experimental results demonstrate that the processing time of the speech signal of 32ms length with delay estimation of every 10 samples is about 26.3 ms, and almost the same performance as given in [1] is obtained with the implemented system.

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Speech Enhancement Using the Adaptive Noise Canceling Technique with a Recursive Time Delay Estimator (재귀적 지연추정기를 갖는 적응잡음제거 기법을 이용한 음성개선)

  • 강해동;배근성
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.7
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    • pp.33-41
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    • 1994
  • A single channel adaptive noise canceling (ANC) technique with a recursive time delay estimator (RTDE) is presented for removing effects of additive noise on the speech signal. While the conventional method makes a reference signal for the adaptive filter using the pitch estimated on a frame basis from the input speech, the proposed method makes the reference signal using the delay estimated recursively on a sample-by-sample basis. As the RTDEs, the recursion formulae of autocorrelation function (ACF) and average magnitude difference function (AMDF) are derived. The normalized least mean square (NLMS) and recursive least square (RLS) algorithms are applied for adaptation of filter coefficients. Experimental results with noisy speech demonstrate that the proposed method improves the perceived speech quality as well as the signal-to-noise ratio and cepstral distance when compared with the conventional method.

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