Speech-preserving active noise control: a deep learning approach in reverberant environments

TL;DR

This paper introduces a deep learning-based active noise control system that effectively reduces noise while preserving speech quality in reverberant environments using a convolutional recurrent network and specialized loss functions.

Contribution

It presents an end-to-end deep learning architecture with a novel voice retention loss, improving noise reduction and speech preservation over traditional methods in complex acoustic settings.

Findings

Significantly better noise reduction than FxLMS, especially for non-stationary noise.

Preserves speech naturalness and intelligibility as confirmed by PESQ and STOI evaluations.

Effective in reverberant environments using simulated acoustic scenes.

Abstract

Traditional Active Noise Control (ANC) systems are mostly based on FxLMS algorithms, but such algorithms rely on linear assumptions and are often limited in handling broadband non-stationary noise or nonlinear acoustic paths. Not only that, the traditional method is used to eliminating all signals together, and noise reduction often accidentally damages the voice signal and affects normal communication. To tackle these issues, this study proposes a speech preserving deep learning ANC system, which aims to achieve stable noise reduction while effectively retaining speech in a complex acoustic environment. This study builds an end-to-end control architecture, the core of which adopts a Convolutional Recurrent Network (CRN). The structure uses the long short-term memory (LSTM) network to capture the time-related characteristics of acoustic signals. Combined with complex spectrum mapping (CSM) technology, the nonlinear distortion problem is effectively solved. In order to retain useful voice while removing noise, this study also designs a special voice retention loss function. This design guidance model selectively retains the target voice while suppressing environmental noise by identifying the characteristics of the spectrum structure. In addition, in order to verify whether the system is effective in real scenes, we use the Image Source Method (ISM) to build a high-fidelity acoustic simulation environment, which also simulates the real reverberation effect. Experimental results demonstrate that the proposed Deep ANC system achieves significantly better noise reduction than the traditional FxLMS algorithm, especially for non-stationary noises like crowd babble. Meanwhile, PESQ and STOI based evaluations confirm that the system preserves both the naturalness and intelligibility of the target speech.