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<p>This paper proposes an innovative method for lossless audio compression utilizing artificial feed-forward neural networks. By implementing dynamic data segregation and adaptive transformations across three successive stages, our approach achieves superior compression while maintaining audio integrity. This technique not only allows for the separation of encoders and decoders but also seeks to outperform current lossless audio compressors across multiple audio formats. The efficacy and robustness of the proposed model are evaluated through various audio quality measurement metrics, demonstrating an optimal balance between compression ratio, accuracy, and computational efficiency.</p>

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Improved audio compression through advanced adaptive data processing and distribution in an ANN framework

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Asish Debnath,
  • Uttam Kr. Mondal

摘要

This paper proposes an innovative method for lossless audio compression utilizing artificial feed-forward neural networks. By implementing dynamic data segregation and adaptive transformations across three successive stages, our approach achieves superior compression while maintaining audio integrity. This technique not only allows for the separation of encoders and decoders but also seeks to outperform current lossless audio compressors across multiple audio formats. The efficacy and robustness of the proposed model are evaluated through various audio quality measurement metrics, demonstrating an optimal balance between compression ratio, accuracy, and computational efficiency.