End-to-End Multi-Task Learning for Adjustable Joint Noise Reduction and Hearing Loss Compensation

Documenti analoghi

• Controllable joint noise reduction and hearing loss compensation using a differentiable auditory model
  di: Gonzalez, Philippe, et al.
  Pubblicazione: (2025)
• An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
  di: Guimarães, Heitor R., et al.
  Pubblicazione: (2024)
• Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation
  di: Yuan, Hui-Guan, et al.
  Pubblicazione: (2025)
• End-to-End Joint ASR and Speaker Role Diarization with Child-Adult Interactions
  di: Xu, Anfeng, et al.
  Pubblicazione: (2026)
• End-to-End Diarization utilizing Attractor Deep Clustering
  di: Palzer, David, et al.
  Pubblicazione: (2025)