Data from: A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.

整个动物界的各类动物均擅长从混杂的背景噪声中提取出目标单一声音，但当前最先进的信号处理算法即便仅面对轻微的背景噪声，也难以可靠处理语音信号。近期针对人类的心理物理实验以及动物模型的电生理记录研究均表明，大脑已进化出适配自然声音所蕴含的时频谱调制（spectro-temporal modulations）受限域的声音处理机制。本文介绍了一种新型单麦克风降噪算法——时频谱检测-重构（spectro-temporal detection–reconstruction，简称STDR），该算法基于经过训练的人工神经网络，可检测、提取并重构语音中的时频谱特征。STDR可在显著降低背景噪声水平的同时，保留目标语音的音质，并提升语音可懂度的评估效果。此外，通过利用语音中显著的时间相关性，STDR算法还可基于对未来语音特征的预测开展处理，在保持相近性能的同时，将固有处理吞吐延迟降至最低。在广泛的信噪比范围内，STDR的表现均优于同类主流先进算法，且具备应用于助听器、自动语音识别等实时场景的潜力。