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基于深神經(jīng)網(wǎng)絡(luò )的線(xiàn)性回波抵消與不完全傳遞函數的凸重構
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華池縣職業(yè)中等專(zhuān)業(yè)學(xué)校

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TN914

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Linear Echo Cancellation and Convex Reconstruction of Incomplete Transfer Function Based on DNN
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    摘要:

    開(kāi)發(fā)了一種在時(shí)頻域中工作算法,該算法假設只有在逼近信號不活躍的頻率條件下,才能估計出各自的傳遞函數。該算法利用在混合信號上訓練的深神經(jīng)網(wǎng)絡(luò )來(lái)檢測逼近信號的活動(dòng),在未檢測到任何活動(dòng)頻率情況下,使用常規頻域最小二乘法估計聲波傳遞函數。對于出現的傳遞函數(ITF)估計不完整問(wèn)題,該算法通過(guò)模糊時(shí)間域內ITF最稀疏表示來(lái)完成,將軟閾值函數應用于時(shí)間域,由軟閾值函數自適應完成,同時(shí)使用過(guò)采樣來(lái)提高精度。實(shí)驗結果表明:在活躍頻率為80%時(shí),該算法比傳統算法收斂速度快50%左右。語(yǔ)音實(shí)驗中,改進(jìn)ADMM算法耗時(shí)0.125s,明顯優(yōu)于傳統算法。為語(yǔ)音傳輸業(yè)務(wù)中存在回聲消除問(wèn)題提供了新思路。

    Abstract:

    An algorithm is developed in the time-frequency domain. It is assumed that the transfer function can be estimated only under the condition that the approximation signal is inactive. The algorithm uses a deep neural network trained on the mixed signal to detect the activity of the approximation signal, and the conventional frequency domain least squares method is used to estimate the acoustic wave transfer function without detecting any active frequencies. For the incomplete problem of the proposed transfer function (ITF) estimation, the algorithm is completed by the most sparse representation of the ITF in the fuzzy time domain. The soft threshold function is applied to the time domain, adaptively completed by the soft threshold function, and oversampling is used to improve the accuracy. . The experimental results show that the algorithm converges by about 50% faster than the traditional algorithm when the active frequency is 80%. In the speech experiment, the improved ADMM algorithm takes 0.125s, which is obviously superior to the traditional algorithm. It provides a new idea for the problem of echo cancellation in voice transmission services.

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文鎖.基于深神經(jīng)網(wǎng)絡(luò )的線(xiàn)性回波抵消與不完全傳遞函數的凸重構計算機測量與控制[J].,2020,28(6):108-112.

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歷史
  • 收稿日期:2019-09-18
  • 最后修改日期:2019-10-14
  • 錄用日期:2019-10-14
  • 在線(xiàn)發(fā)布日期: 2020-06-17
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