DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering
Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
11.10.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Complex-valued processing has brought deep learning-based speech enhancement
and signal extraction to a new level. Typically, the process is based on a
time-frequency (TF) mask which is applied to a noisy spectrogram, while complex
masks (CM) are usually preferred over real-valued masks due to their ability to
modify the phase. Recent work proposed to use a complex filter instead of a
point-wise multiplication with a mask. This allows to incorporate information
from previous and future time steps exploiting local correlations within each
frequency band. In this work, we propose DeepFilterNet, a two stage speech
enhancement framework utilizing deep filtering. First, we enhance the spectral
envelope using ERB-scaled gains modeling the human frequency perception. The
second stage employs deep filtering to enhance the periodic components of
speech. Additionally to taking advantage of perceptual properties of speech, we
enforce network sparsity via separable convolutions and extensive grouping in
linear and recurrent layers to design a low complexity architecture. We further
show that our two stage deep filtering approach outperforms complex masks over
a variety of frequency resolutions and latencies and demonstrate convincing
performance compared to other state-of-the-art models. |
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DOI: | 10.48550/arxiv.2110.05588 |