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 ca...

整个动物界的生物均擅长从混杂背景噪声中提取目标单一声信号，而当前最先进的信号处理算法即便仅面对微弱背景噪声，也难以准确处理语音信号。此前针对人类开展的心理物理学实验与动物模型的电生理记录均显示，大脑已演化出适配自然声音固有特定时频调制范围的声信号处理机制。本文介绍了一种新型单麦克风降噪算法——时频检测-重构（spectro-temporal detection–reconstruction, STDR），该算法依托经训练的人工神经网络，实现对语音中时频特征的检测、提取与重构。STDR可在显著抑制背景噪声的同时，保留目标语音的音质，并提升语音可懂度的评估指标。此外，通过利用语音中显著的时间相关性，STDR算法可……