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.