Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency of the entire system. To address these shortcomings, we propose a fully-convolutional time-domain audio separation network (Conv-TasNet), a deep learning framework for end-to-end time-domain speech…

• NS
  National Science Foundation

  Award: CAREER
• PC
  Pew Charitable Trusts

  Award: DC014279
• NI
  National Institutes of Health
• NI
  National Institute on Deafness and Other Communication Disorders

  Award: DC014279