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About this paper
Peter Ochieng
Peter Ochieng from the University of Cambridge provides a comprehensive review of deep neural network techniques for monaural speech enhancement and separation, analyzing 500 papers from 2018-2022 to document the evolution from traditional methods to modern DNNs. This work systematically outlines the pipeline from feature extraction to objective functions, identifying prevalent challenges and emerging solutions in the field.
Key insights
- There is a growing preference for time-domain features over traditional DFT-based features in DNNs for speech enhancement and separation, offering marginal performance gains in some scenarios.
- Model architectures have evolved to capture long-term dependencies more effectively, moving from RNNs to TCNs and Transformers, while various compression techniques are crucial for practical deployment.
- The field is increasingly adopting perceptually motivated objective functions (e.g., SI-SDR, STOI) and exploring unsupervised learning methods (e.g., MixIT, unsupervised GANs) to address data scarcity and improve speech quality.
Summary by alphaXiv.
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