RSSNet is a deep separation network that decomposes single-channel noisy Raman spectra into pure component spectra, outperforming sparse regression by over 4 dB on synthetic data and generalizing from synthetic training to real mineral powder mixtures.
Deep clustering: Discriminative embeddings for segmentation and separation,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
SR-CorrNet introduces an asymmetric TF-domain architecture with separation-reconstruction strategy and correlation-to-filter estimation that yields consistent gains on WSJ0-Mix, WHAMR!, and LibriCSS under anechoic, noisy-reverberant, and real-recorded conditions.
citing papers explorer
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A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing
RSSNet is a deep separation network that decomposes single-channel noisy Raman spectra into pure component spectra, outperforming sparse regression by over 4 dB on synthetic data and generalizing from synthetic training to real mineral powder mixtures.
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Asymmetric Encoder-Decoder Based on Time-Frequency Correlation for Speech Separation
SR-CorrNet introduces an asymmetric TF-domain architecture with separation-reconstruction strategy and correlation-to-filter estimation that yields consistent gains on WSJ0-Mix, WHAMR!, and LibriCSS under anechoic, noisy-reverberant, and real-recorded conditions.