摘要
现有助听器往往将声反馈、噪声和混响问题独立优化,约束了其性能上限,为此提出了一种两阶段端到端深度学习联合声反馈抑制和去噪去混响方法。该方法首先在助听器临界稳定工作状态通过闭环系统仿真构造大量的带噪带混响带声反馈的数据,其次通过离线联合训练的方式完成两阶段网络模型预训练,最后将预训练好的模型应用于闭环系统中实现低时延声反馈、噪声和混响的同步抑制。以实录声反馈路径进行助听器系统闭环仿真测试的客观实验结果表明,相比于传统处理算法、只考虑混响和噪声的模型和只采用单阶段网络训练的模型,所提方法在语音质量感知评估测度、扩展的短时客观可懂度和加权频带分段信噪比等客观指标上均具有显著优势。
Existing hearing aids often handle acoustic feedback,noise,and reverberation separately,thus limiting the upper performance bound of hearing aids.This paper proposes a two-stage end-to-end learning approach for joint optimization of feedback suppression,noise reduction,and dereverberation.Specially,a great number of paired data using simulated closed-loop systems in noisy and reverberant environments is generated,and these paired data were then used to train the proposed two-stage deep-learning models jointly.Finally,the pretrained models were inserted into the closed-loop system of a hearing aid to online process feedback,noise,and reverberation simultaneously in a low-latency mode.Experiments using two recorded feedback paths to evaluate the performance of the proposed approach were conducted,and experimental results showed that the proposed approach achieved the best performance in terms of PESQ,ESTOI,and frequency-weighted SNR when compared with the traditional approach,separate training models,and single-stage model.
作者
王梅煌
章辉勇
徐晨阳
李晓东
郑成诗
WANG Meihuang;ZHANG Huiyong;XU Chenyang;LI Xiaodong;ZHENG Chengshi(Key Laboratory of Noise and Vibration Research,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)
出处
《声学学报》
EI
CAS
CSCD
北大核心
2024年第6期1215-1225,共11页
Acta Acustica
基金
国家重点研发计划项目(2021YFB3201702)
国家自然科学基金项目(12074403)资助。
关键词
助听器
深度神经网络
联合训练
声反馈抑制
Hearing aids
Deep neural networks
Joint training
Acoustic feedback suppression
