立体声回声控制的深度混合模型

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Yang Liu, Sichen Liu, Feiran Yang, Jun Yang
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引用次数: 0

摘要

本文提出了一种用于立体声回声消除(SAEC)的深度混合模型。该模型分为两个阶段,即基于深度学习的卡尔曼滤波器(DeepKalman)和基于门控卷积递归网络(GCRN)的后置滤波器,它们以端到端的方式进行联合训练。所提出的 DeepKalman 滤波器与传统滤波器的区别有两个方面。首先,DeepKalman 滤波器的输入信号是原始的两个远端信号和直接从麦克风信号估算出的非线性参考信号的组合。其次,利用低复杂度的递归神经网络来估计过程噪声的协方差,从而增强 DeepKalman 滤波器的跟踪能力。在第二阶段,我们采用 GCRN,通过估计应用于第一阶段输出信号的复杂掩码来抑制残余回声和噪声。计算机仿真证实,与现有的 SAEC 算法相比,所提出的方法具有性能优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Deep Hybrid Model for Stereophonic Acoustic Echo Control

A Deep Hybrid Model for Stereophonic Acoustic Echo Control

This paper proposes a deep hybrid model for stereophonic acoustic echo cancellation (SAEC). A two-stage model is considered, i.e., a deep-learning-based Kalman filter (DeepKalman) and a gated convolutional recurrent network (GCRN)-based postfilter, which are jointly trained in an end-to-end manner. The difference between the proposed DeepKalman filter and the conventional one is twofold. First, the input signal of the DeepKalman filter is a combination of the original two far-end signals and the nonlinear reference signal estimated from the microphone signal directly. Second, a low-complexity recurrent neural network is utilized to estimate the covariance of the process noise, which can enhance the tracking capability of the DeepKalman filter. In the second stage, we adopt GCRN to suppress residual echo and noise by estimating complex masks applied to the output signal of the first stage. Computer simulations confirm the performance advantage of the proposed method over existing SAEC algorithms.

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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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