Yet adaptedness is often difficult to test. Why do we see or hear the way we do? Perception is believed to be adapted to the world, shaped over evolution and development to help us survive in our ecological niche. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can reveal the real-world constraints that shape perception. But when trained in unnatural environments without reverberation, noise or natural sounds, these performance characteristics deviated from those of humans. In simulated experiments, the model exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, biases for sound onsets and limits on localization of concurrent sources. The resulting model localized accurately in realistic conditions with noise and reverberation. To better understand real-world localization, we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. Localization in real-world conditions is challenging, as echoes provide erroneous information and noises mask parts of target sounds. Mammals localize sounds using information from their two ears.

Sound normalizer software training