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This study introduces a learnable spectral dimension compression method for effectively compressing spectral features along frequency axes. It emphasizes preserving high resolution in low frequencies, while allowing for more adaptable compression of information in high frequencies. Empirical findings confirm the seamless integration of the suggested approach with diverse full-spectrum speech improvement models, yielding enhanced outc
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