用于声学回声消除的扬声器和电话感知卷积变压器网络

Chang Han, Weiping Tu, Yuhong Yang, Jingyi Li, Xinhong Li
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引用次数: 1

摘要

最近的研究表明,基于深度学习(DL)的声学回声消除(AEC)方法在背景噪声和非线性失真场景中的有效性。然而,内容和说话者的变化降低了这种基于DL的AEC模型的性能。在这项研究中,我们提出了一个AEC模型,该模型以语音和说话人身份特征作为辅助输入,并提出了一种复杂的双路径卷积变换网络(DPCTNet)。给定输入信号,由作为自监督预训练模型的对比预测编码网络提取的语音和说话者身份特征,以及由短时间傅立叶变换生成的复频谱被视为DPCTNet的频谱模式输入。此外,DPCTNet应用了通过插入双路径转换器改进的编码器-解码器架构,以有效地对单个帧中提取的输入和连续帧之间的相关性进行建模。对比实验结果表明,通过明确考虑语音和说话人身份特征,可以提高AEC的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speaker- and Phone-aware Convolutional Transformer Network for Acoustic Echo Cancellation
Recent studies indicate the effectiveness of deep learning (DL) based methods for acoustic echo cancellation (AEC) in background noise and nonlinear distortion scenarios. However, content and speaker variations degrade the performance of such DL-based AEC models. In this study, we propose a AEC model that takes phonetic and speaker identities features as auxiliary inputs, and present a complex dual-path convolutional transformer network (DPCTNet). Given an input signal, the phonetic and speaker identities features extracted by the contrastive predictive coding network that is a self-supervised pre-training model, and the complex spectrum generated by short time Fourier transform are treated as the spectrum pattern inputs for DPCTNet. In addition, the DPCTNet applies an encoder-decoder architecture improved by inserting a dual-path transformer to effectively model the extracted inputs in a single frame and the dependence between consecutive frames. Com-parative experimental results showed that the performance of AEC can be improved by explicitly considering phonetic and speaker identities features.
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