基于WaveNet的耳过和毛细胞转导模型的近似估计
1Institute of Communication Acoustics, Ruhr-Universität Bochum, Bochum, Germany.
The Journal of the Acoustical Society of America
|July 12, 2023
概括
这项研究引入了一个WaveNet模型来近似听觉系统功能,显著加快计算听觉模型的速度. 这种高效的模型准确地预测了内部毛细胞的潜力,使生物启发的音频处理速度更快.
科学领域:
- 听觉神经科学 听觉神经科学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 计算听觉模型提供了对听觉的洞察力,但是在计算上是密集的.
- 准确的听觉模型对于开发生物灵感的语音和音频处理算法至关重要.
- 通常需要高效的执行,这对复杂的模型构成挑战.
研究的目的:
- 开发一种广泛使用的听觉模型的耳过和内毛细胞 (IHC) 传导阶段的计算效率近似.
- 为了利用WaveNet来接近关键的审计功能.
- 为了使研究和应用更快,更容易获得的听觉建模.
主要方法:
- 一个WaveNet模型被训练来近似正常听觉的耳过和IHC转导阶段.
- 培训使用了各种音频信号 (语音,音乐) 的大量数据集,跨越各种声音压力水平 (SPL) 和频率.
- 该模型被评估在未见的噪音语音,音乐,正弦音和点击信号上.
主要成果:
- 波网模型准确地预测了IHC受体对各种刺激的潜力.
- 实现的处理时间比优化的参考实现快250倍.
- 在一系列SPL (30-100 dB) 和特征频率 (125 Hz - 8 kHz) 中表现出高精度.
结论:
- 波浪网提供了对关键听觉模型组件的高效准确近似.
- 该模型的速度和准确性促进了实时生物灵感音频处理中的应用.
- 它的区分性使其适合基于深度学习的语音和音频增强算法.
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