2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References
Samuele Cornell, Matthew Maciejewski
Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real gap of synthetic training data,…
Exploiting Noise Inseparability for Weakly-Supervised Discriminative Speech Denoising Using Noisy Targets
Matthew Maciejewski, Samuele Cornell
Speech denoising is an often necessary step not only for human listening, but also for downstream processing by systems lacking robustness to noisy, real-world acoustic conditions.…
Single-Microphone Audio Point Source Discriminative Localization From Reverberation Late Tail Estimation
Matthew Maciejewski
Location information can be a valuable signal for audio segmentation tasks, especially as a complement to methods focusing on the content or qualities of the sources. Though audio…
Ring Mixing with Auxiliary Signal-to-Consistency-Error Ratio Loss for Unsupervised Denoising in Speech Separation
Matthew Maciejewski, Samuele Cornell
Noisy speech separation systems are typically trained on fully-synthetic mixtures, limiting generalization to real-world scenarios. Though training on mixtures of in-domain (thus o…