2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-25.5
Paper Title SOURCE-AWARE NEURAL SPEECH CODING FOR NOISY SPEECH COMPRESSION
Authors Haici Yang, Kai Zhen, Indiana University, United States; Seungkwon Beack, Electronics and Telecommunications Research Institute, South Korea; Minje Kim, Indiana University, United States
SessionAUD-25: Signal Enhancement and Restoration 2: Audio Coding and Restoration
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-AMCT] Audio and Speech Modeling, Coding and Transmission
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper introduces a novel neural network-based speech coding system that can handle noisy speech effectively. The proposed source-aware neural audio coding (SANAC) system harmonizes a deep autoencoder-based source separation model and a neural coding system, so that it can explicitly perform source separation and coding in the latent space. An added benefit of this system is that the codec can allocate different amount of bits to the underlying sources, so that the more important source sounds better in the decoded signal. We target the use case where the user on the receiver side cares the quality of the non-speech components in the speech communication, while the speech source still carries the most important information. Both objective and subjective evaluation tests show that SANAC can recover the original noisy speech in a better quality than the baseline neural audio coding system, which is with no source-aware coding mechanism.