The performance study of NLMS algorithm for acoustic echo cancellation

Tyagi, Ranbeer; Singh, Roop; Tiwari, Rahul

doi:10.1109/icomicon.2017.8279017

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Since there is a reference signal named the far-end, adaptive filters (AF) are always employed for acoustic echo cancellation (AEC) [3]. Several classical AF algorithms have been proposed, such as least mean square (LMS) [4], normalized LMS (NLMS) [5], block LMS (BLMS) [6], and etc. Among them the NLMS algorithm family is most widely used due to its relatively robust performance and low complexity, such as the frequency domain block adaptive filter (FDBAF) [7] and the multidelay block frequency domain (MDF) adaptive filter [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

Ma,

Yang,

Gong

et al. 2021

Preprint

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Acoustic Echo Cancellation (AEC) plays a key role in speech interaction by suppressing the echo received at microphone introduced by acoustic reverberations from loudspeakers. Since the performance of linear adaptive filter (AF) would degrade severely due to nonlinear distortions, background noises, and microphone clipping in real scenarios, deep learning has been employed for AEC for its good nonlinear modelling ability. In this paper, we constructed an end-to-end multi-scale attention neural network for AEC. Temporal convolution is first used to transform waveform into spectrogram. The spectrograms of the far-end reference and the near-end mixture are concatenated, and fed to a temporal convolution network (TCN) with stacked dilated convolution layers. Attention mechanism is performed among these representations from different layers to adaptively extract relevant features by referring to the previous hidden state in the encoder long short-term memory (LSTM) unit. The representations are weighted averaged and fed to the encoder LSTM for the near-end speech estimation. Experiments show the superiority of our method in terms of the echo return loss enhancement (ERLE) for single-talk periods and the perceptual evaluation of speech quality (PESQ) score for double-talk periods in background noise and nonlinear distortion scenarios.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

Ma,

Yang,

Gong

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Since there is a reference signal named the far-end, adaptive filters are always employed for estimating acoustic echo, thus achieving acoustic echo cancellation (AEC) by subtracting the estimation from the mixture [3]. Several classical algorithms based on adaptive filtering (AF) have been proposed, such as least mean square (LMS) [4], normalized LMS (NLMS) [5], block LMS (BLMS) [6], and etc. Among them the NLMS algorithm family is most widely used due to its relatively robust performance and low complexity, such as the frequency domain block adaptive filter (FDBAF) [7] used in WebRTC and the multidelay block frequency domain (MDF) [8] adaptive filter used in Speex.…”

Section: Introductionmentioning

confidence: 99%

EchoFilter: End-to-End Neural Network for Acoustic Echo Cancellation

Ma,

Yang,

Gong

et al. 2021

Preprint

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Acoustic Echo Cancellation (AEC) whose aim is to suppress the echo originated from acoustic coupling between loudspeakers and microphones, plays a key role in voice interaction. Linear adaptive filter (AF) is always used for handling this problem. However, since there would be some severe effects in real scenarios, such nonlinear distortions, background noises, and microphone clipping, it would lead to considerable residual echo, giving poor performance in practice. In this paper, we propose an end-to-end network structure for echo cancellation, which is directly done on time-domain audio waveform. It is transformed to deep representation by temporal convolution, and modelled by Long Short-Term Memory (LSTM) for considering temporal property. Since time delay and severe reverberation may exist at the near-end with respect to the far-end, a local attention is employed for alignment. The network is trained using multitask learning by employing an auxiliary classification network for double-talk detection. Experiments show the superiority of our proposed method in terms of the echo return loss enhancement (ERLE) for single-talk periods and the perceptual evaluation of speech quality (PESQ) score for double-talk periods in background noise and nonlinear distortion scenarios.

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“…Since there is a reference signal representing the source of echo, adaptive filters are always employed for acoustic echo cancellation (AEC). There are many adaptive algorithms available, such as least mean square (LMS) [3], normalized LMS (NLMS) [4], block LMS (BLMS) [5], and etc. Each has its own merits and special applications.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network

Ma,

Huang,

Zhao

et al. 2020

Preprint

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Acoustic Echo Cancellation (AEC) plays a key role in voice interaction. Due to the explicit mathematical principle and intelligent nature to accommodate conditions, adaptive filters with different types of implementations are always used for AEC, giving considerable performance. However, there would be some kinds of residual echo in the results, including linear residue introduced by mismatching between estimation and the reality and non-linear residue mostly caused by non-linear components on the audio devices. The linear residue can be reduced with elaborate structure and methods, leaving the non-linear residue intractable for suppression. Though, some non-linear processing methods have already be raised, they are complicated and inefficient for suppression, and would bring damage to the speech audio. In this paper, a fusion scheme by combining adaptive filter and neural network is proposed for AEC. The echo could be reduced in a large scale by adaptive filtering, resulting in little residual echo. Though it is much smaller than speech audio, it could also be perceived by human ear and would make communication annoy. The neural network is elaborately designed and trained for suppressing such residual echo. Experiments compared with prevailing methods are conducted, validating the effectiveness and superiority of the proposed combination scheme.

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The performance study of NLMS algorithm for acoustic echo cancellation

Cited by 7 publications

References 16 publications

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

Multi-Scale Attention Neural Network for Acoustic Echo Cancellation

EchoFilter: End-to-End Neural Network for Acoustic Echo Cancellation

Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network

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