Zhang Huiyong, Moore Brian C J, Jiang Feng, Diao Mingfang, Ji Fei, Li Xiaodong, Zheng Chengshi
Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Trends Hear. 2025 Jan-Dec;29:23312165241309301. doi: 10.1177/23312165241309301.
Wide dynamic range compression (WDRC) and noise reduction both play important roles in hearing aids. WDRC provides level-dependent amplification so that the level of sound produced by the hearing aid falls between the hearing threshold and the highest comfortable level of the listener, while noise reduction reduces ambient noise with the goal of improving intelligibility and listening comfort and reducing effort. In most current hearing aids, noise reduction and WDRC are implemented sequentially, but this may lead to distortion of the amplitude modulation patterns of both the speech and the noise. This paper describes a deep learning method, called Neural-WDRC, for implementing both noise reduction and WDRC, employing a two-stage low-complexity network. The network initially estimates the noise alone and the speech alone. Fast-acting compression is applied to the estimated speech and slow-acting compression to the estimated noise, but with a controllable residual noise level to help the user to perceive natural environmental sounds. Neural-WDRC is frame-based, and the output of the current frame is determined only by the current and preceding frames. Neural-WDRC was compared with conventional slow- and fast-acting compression and with signal-to-noise ratio (SNR)-aware compression using objective measures and listening tests based on normal-hearing participants listening to signals processed to simulate the effects of hearing loss and hearing-impaired participants. The objective measures demonstrated that Neural-WDRC effectively reduced negative interactions of speech and noise in highly non-stationary noise scenarios. The listening tests showed that Neural-WDRC was preferred over the other compression methods for speech in non-stationary noises.
宽动态范围压缩（WDRC）和降噪在助听器中都起着重要作用。WDRC提供与电平相关的放大，以使助听器产生的声音电平介于听力阈值和聆听者的最高舒适电平之间，而降噪则以提高可懂度、聆听舒适度和减少聆听努力为目标来降低环境噪声。在大多数当前的助听器中，降噪和WDRC是顺序实现的，但这可能会导致语音和噪声的调幅模式失真。本文描述了一种名为Neural-WDRC的深度学习方法，用于同时实现降噪和WDRC，采用两阶段低复杂度网络。该网络最初单独估计噪声和语音。对估计出的语音应用快速压缩，对估计出的噪声应用慢速压缩，但保留可控的残余噪声电平，以帮助用户感知自然环境声音。Neural-WDRC基于帧，当前帧的输出仅由当前帧和前一帧决定。基于正常听力参与者聆听经处理以模拟听力损失影响的信号以及听力受损参与者，使用客观测量和听力测试，将Neural-WDRC与传统的慢速和快速压缩以及信噪比（SNR）感知压缩进行了比较。客观测量表明，Neural-WDRC在高度非平稳噪声场景中有效减少了语音和噪声的负面相互作用。听力测试表明，在非平稳噪声环境下，对于语音，Neural-WDRC比其他压缩方法更受青睐。
