{"total":15,"rows":[{"id":"2110.09625","title":"Personalized Speech Enhancement: New Models and Comprehensive Evaluation","created":"2021-10-18","cited_by_count":21,"area":null},{"id":"2202.08862","title":"RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing","created":"2022-02-17","cited_by_count":20,"area":null},{"id":"2211.02542","title":"Self-Supervised Learning for Speech Enhancement through Synthesis","created":"2022-11-04","cited_by_count":10,"area":null},{"id":"2304.04974","title":"Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR","created":"2023-04-11","cited_by_count":10,"area":null},{"id":"2110.10103","title":"Continual self-training with bootstrapped remixing for speech enhancement","created":"2021-10-19","cited_by_count":6,"area":null},{"id":"2211.12623","title":"SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2305.09994","title":"BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions","created":"2023-05-17","cited_by_count":4,"area":null},{"id":"2411.02019","title":"Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2409.12415","title":"Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues","created":"2024-09-19","cited_by_count":2,"area":null},{"id":"2210.15368","title":"A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech","created":"2022-10-27","cited_by_count":1,"area":null},{"id":"2408.06911","title":"Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2111.04436","title":"SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points","created":"2021-11-08","cited_by_count":0,"area":null},{"id":"2402.01413","title":"Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2602.02413","title":"Masked Autoencoders as Universal Speech Enhancer","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.09187","title":"The Costs of Reproducibility in Music Separation Research: a Replication of Band-Split RNN","created":"2026-03-10","cited_by_count":0,"area":null}],"next_cursor":null}