A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-Temporal features

Abstract

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.