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基于时域和时频域联合优化的语音增强算法

Speech Enhancement Algorithm Based on Time-Domain and Time-Frequency Joint Optimization

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【作者】 杨帆李军锋颜永红

【Author】 YANG Fan;LI Junfeng;YAN Yonghong;Key Laboratory of Speech Acoustics and Content Understanding,Institute of Acoustics,Chinese Academy of Sciences;University of Chinese Academy of Sciences;Xinjiang Laboratory of Minority Speech and Language Information Processing,Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences;

【机构】 中国科学院声学研究所语言声学与内容理解重点实验室中国科学院大学中国科学院新疆理化技术研究所新疆民族语音语言信息处理实验室

【摘要】 基于深度学习的语音增强算法从带噪语音信号的时域或者时频域中恢复出干净的语音信号。然而,时域和时频域的增强算法都有自己的优点和不足。针对这一问题,本文提出了一种基于时域和时频域联合优化的语音增强算法。在生成对抗网络框架下,分别构建了时域和时频域的学习目标。在训练过程中,利用跳跃连接搭建了深层次的网络结构,通过对不同领域学习目标的联合优化,获得了语音增强性能的改善。实验结果表明:相比基线模型,本文提出的算法在多个客观评价指标上都具有更好的表现。

【Abstract】 Deep learning-based speech enhancement algorithm recovers clean speech signals from the time domain or time-frequency domain of noisy speech signals. However, both time-domain and time-frequency-domain methods have their own advantages and disadvantages. To solve this problem, this paper proposes a speech enhancement algorithm based on joint optimization of time domain and time-frequency domain. In the framework of generative adversarial network, the learning objectives in time domain and time-frequency domain are constructed respectively. In the training process, the skip connection is used to build a deep network structure, and the improvement of speech enhancement performance is obtained through joint optimization of learning goals in different fields. The experimental results show that: compared with the baseline models, the proposed algorithm has better performance on multiple objective evaluation metrics.

【基金】 国家重点研发计划项目(编号:2020YFC2004100);国家自然科学基金项目(编号:11911540067)
  • 【文献出处】 网络新媒体技术 ,Network New Media Technology , 编辑部邮箱 ,2021年05期
  • 【分类号】TN912.35
  • 【下载频次】244
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