WHAM

The 'WHAM!' dataset/benchmark contains mixtures of speech and is used to evaluate the performance of noisy speech separation systems.

Papers using WHAM (15)

Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers2025 · 1 citesRing Mixing with Auxiliary Signal-to-Consistency-Error Ratio Loss for Unsupervised Denoising in Speech Separation2026A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References2025Dynamic Slimmable Networks for Efficient Speech Separation2025Listen to Extract: Onset-Prompted Target Speaker Extraction2025MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions2023 · 64 citesResource-Efficient Separation Transformer2022 · 11 citesSPMamba: State-space model is all you need in speech separation2024 · 8 citesExploring Self-Attention Mechanisms for Speech Separation2022 · 2 citesMulti-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning2023 · 1 citesNoise-Aware Speech Separation with Contrastive Learning2023 · 1 citesMossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation2023 · 1 citesUSEF-TSE: Universal Speaker Embedding Free Target Speaker Extraction2024 · 1 citesAudio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model2023Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain2021