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A scalable codec for bone-conducted speech based on generative and diffusion models.

Xiaolong Hu1, Zhe Chen1, Fuliang Yin1

  • 1Department of Information and Communication Engineering, Dalian University of Technology (DUT), Dalian, 116023, China.

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Summary
This summary is machine-generated.

A new scalable codec enhances bone-conducted microphone (BCM) speech quality in noisy conditions. This single network approach simplifies architecture while improving BCM speech and supporting variable bitrates.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Audio Engineering

Background:

  • Bone-conducted microphone (BCM) speech quality degrades in noisy environments.
  • Current methods combine BCM codecs with bandwidth extension, creating complex systems.

Purpose of the Study:

  • To propose a scalable codec for BCM speech using generative and diffusion models.
  • To simplify system architecture while improving BCM speech quality.

Main Methods:

  • Developed a specialized codec architecture to encode BCM speech and complement high-frequency components.
  • Introduced a feature extraction block to address memory capacity limitations in deep networks.
  • Implemented a refinement block to enhance reconstructed speech signals.
  • Utilized a U-Net based diffusion probability model for upsampling audio from 8 kHz to 20 kHz (48 kHz sampling rate).

Main Results:

  • The proposed method effectively encodes and enhances BCM speech quality within a single network.
  • The codec supports various bitrate settings without requiring architectural changes or retraining.
  • Dynamic data transmission adjustment based on network load is feasible.
  • Simulation experiments confirmed the method's effectiveness.

Conclusions:

  • The novel codec offers a simplified and scalable solution for improving BCM speech quality.
  • This approach integrates encoding and enhancement, offering flexibility for different network conditions and bitrates.