Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Elife. 2022 May 26;11:e75090. doi: 10.7554/eLife.75090.
In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.
在几乎所有的自然环境中，声音都会被附近的物体反射，产生许多原始声音的延迟和失真副本，称为混响。我们的大脑通常能很好地处理混响，使我们能够识别声源，而不管其环境如何。相比之下，混响会给语音识别算法和听力受损的人带来严重的困难。本研究探讨了听觉系统如何应对混响。我们训练了一个线性模型，从模拟的混响对应物中恢复出一组丰富的自然无声声音。该模型神经元通过为更混响的空间扩展其感受野的抑制成分来实现这一点，并且以频率依赖的方式实现。在同一模拟混响环境中，雪貂的听觉皮层神经元的反应中观察到了这些预测的效果。总之，这些结果表明，听觉皮层神经元通过以与去混响一致的方式调整其滤波特性来适应混响。
