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使用深度学习驱动的源分离技术增强音乐识别,用于耳植入物用户.

Yuh-Jer Chang1, Ji-Yan Han1, Wei-Chung Chu1

  • 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.

The Journal of the Acoustical Society of America
|March 1, 2024
PubMed
概括
此摘要是机器生成的。

这项研究通过应用自我调整的源分离技术,增强了耳植入物 (CI) 用户的音乐听力. 新方法显著提高了声音质量和特征识别,使CI用户在现场音乐会等复杂环境中受益.

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科学领域:

  • 听力学 听力学是指听力学.
  • 信号处理 信号处理
  • 生物医学工程 生物医学工程

背景情况:

  • 耳植入物 (CI) 对于听力恢复至关重要,但由于电极有限和音乐特征识别差,它们提供了不理想的音乐听力体验.
  • 现有的CI技术难以准确处理复杂的音乐音频信号,从而降低了用户的音乐欣赏质量.
  • 提高CI用户的音乐感知是一个重大挑战,影响整体生活质量和听觉参与.

研究的目的:

  • 开发和评估一种具有自我调整能力的新型源分离技术,以改善耳植入物 (CI) 用户的音乐听力.
  • 客观和主观地评估拟议方法在提高音乐信号清晰度方面与已建立的基线模型对比的性能.
  • 确定个性化信号分离技术在克服音乐感知当前CI技术局限性的潜力.

主要方法:

  • 应用了具有自我调整功能的源分离技术来处理CI用户的音乐信号.
  • 进行了客观分析,测量了源与扭曲,源与干扰以及源与工件的比率.
  • 通过使用隐藏参考和测试的多刺激测试进行主观分析,以评估用户的感知.

主要成果:

  • 客观分析显示,与Demucs基线相比,源与扭曲 (4.88dB),源与干扰 (5.92dB) 和源与工件 (15.28dB) 的比率显著改善.
  • 主观分析显示,拟议的方法比传统的VIR6 (声音与仪器比率,6dB) 基线分得大约28.1和26.4分.
  • 个人自适配信号分离方法的性能优于默认基线 (VIR 6dB和0dB),表明音乐识别能力优越.

结论:

  • 拟议的具有自我调整的源分离技术有效地增强了耳植入物 (CI) 用户的音乐听力.
  • 该方法在客观和主观测量方面取得了显著的改进,超过了当前的基线技术.
  • 这种系统为改善耳植入物患者的音乐感知和欣赏提供了一个有希望的方法,特别是在具有挑战性的听觉环境中.