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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Learnable spectral dimension compression mapping for full-band speech enhancement.

Qinwen Hu1, Zhongshu Hou1, Kai Chen1

  • 1Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China qinwen.hu@smail.nju.edu.cn, zhongshu.hou@smail.nju.edu.cn, chenkai@nju.edu.cn, lujing@nju.edu.cn.

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This study introduces a new method to improve full-band speech enhancement by compressing spectral features. This approach enhances performance by better handling imbalanced power in speech signals.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Full-band speech enhancement faces challenges due to imbalanced power spectral density in speech signals.
  • Traditional spectral features, mimicking human hearing, are suboptimal for full-band enhancement.
  • High-frequency information compression is crucial for effective speech enhancement.

Purpose of the Study:

  • To propose a novel learnable spectral dimension compression mapping for full-band speech enhancement.
  • To address the limitations of existing spectral features in handling imbalanced power spectral density.
  • To develop a flexible method for compressing spectral information along the frequency dimension.

Main Methods:

  • A learnable spectral dimension compression mapping was designed.
  • The mapping compresses spectral features along the frequency axis.
  • High-resolution representation is maintained for low frequencies, while high frequencies are compressed flexibly.

Main Results:

  • The proposed spectral dimension compression mapping effectively addresses imbalanced power spectral density.
  • Experimental results demonstrate improved performance in full-band speech enhancement.
  • The method shows versatility and can be integrated with various existing enhancement models.

Conclusions:

  • The proposed learnable spectral dimension compression mapping is an effective technique for full-band speech enhancement.
  • This method offers a flexible and high-performance solution compared to traditional approaches.
  • Integrating this mapping with existing models leads to significant performance gains.