WHAMR!
Emerging24papers using it
2022first seen
The 'WHAMR!' dataset/benchmark contains a collection of mixed speech signals designed to evaluate the performance of speech separation algorithms in challenging acoustic environments.
Papers using WHAMR! (24)
- Exploring Self-attention Mechanisms For Speech SeparationExploring The Integration Of Speech Separation And Recognition With Self-supervised Learning RepresentationReceptive Field Analysis Of Temporal Convolutional Networks For Monaural Speech DereverberationOn Time Domain Conformer Models For Monaural Speech Separation In Noisy Reverberant Acoustic EnvironmentsLibriheavymix: A 20,000-hour Dataset For Single-channel Reverberant Multi-talker Speech Separation, ASR And Speaker DiarizationMagnitude-phase Dual-path Speech Enhancement Network Based On Self-supervised Embedding And Perceptual Contrast Stretch BoostingAsymmetric Encoder-Decoder Based on Time-Frequency Correlation for Speech SeparationMoving Speaker Separation via Parallel Spectral-Spatial ProcessingMC-LExt: Multi-Channel Target Speaker Extraction with Onset-Prompted Speaker Conditioning MechanismReFESS-QI: Reference-Free Evaluation For Speech Separation With Joint Quality And Intelligibility ScoringListen to Extract: Onset-Prompted Target Speaker ExtractionMagnitude-Phase Dual-Path Speech Enhancement Network based on
Self-Supervised Embedding and Perceptual Contrast Stretch BoostingA Two-stage Speaker Extraction Algorithm Under Adverse Acoustic Conditions Using A Single-microphoneTF-GridNet: Integrating Full- and Sub-Band Modeling for Speech
SeparationOn Data Sampling Strategies for Training Neural Network Speech
Separation ModelsExploring the Integration of Speech Separation and Recognition with
Self-Supervised Learning RepresentationMossFormer2: Combining Transformer and RNN-Free Recurrent Network for
Enhanced Time-Domain Monaural Speech SeparationBSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech
Enhancement Network based on Self-Supervised EmbeddingUSEF-TSE: Universal Speaker Embedding Free Target Speaker ExtractionDeformable Temporal Convolutional Networks for Monaural Noisy
Reverberant Speech SeparationA two-stage speaker extraction algorithm under adverse acoustic
conditions using a single-microphoneOn Time Domain Conformer Models for Monaural Speech Separation in Noisy
Reverberant Acoustic EnvironmentsLibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant
Multi-Talker Speech Separation, ASR and Speaker DiarizationX-CrossNet: A complex spectral mapping approach to target speaker
extraction with cross attention speaker embedding fusion