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The neural encoding of sound is the representation of auditory sensation and perception in the nervous system. [1] The complexities of contemporary neuroscience are continually redefined. Thus what is known of the auditory system has been continually changing.
Phase-of-firing code is a neural coding scheme that combines the spike count code with a time reference based on oscillations. This type of code takes into account a time label for each spike according to a time reference based on phase of local ongoing oscillations at low [39] or high frequencies. [40]
The model generates sounds through a neural network based synthesis, employing a WaveNet-style autoencoder to learn its own temporal embeddings from four different sounds. [2] [3] Google then released an open source hardware interface for the algorithm called NSynth Super, [4] used by notable musicians such as Grimes and YACHT to generate experimental music using artificial intelligence.
According to Jeffress, [11] in order to compute the location of a sound source in space from interaural time differences, an auditory system relies on delay lines: the induced signal from an ipsilateral auditory receptor to a particular neuron is delayed for the same time as it takes for the original sound to go in space from that ear to the ...
Consisting of three areas, the outer, middle and inner ear, the auditory periphery acts as a complex transducer that converts sound vibrations into action potentials in the auditory nerve. The outer ear consists of the external ear, ear canal and the ear drum. The outer ear, like an acoustic funnel, helps locating the sound source. [2]
The coding of temporal information in the auditory nerve can be disrupted by two main mechanisms: reduced synchrony and loss of synapses and/or auditory nerve fibers. [186] The impact of disrupted temporal coding on human auditory perception has been explored using physiologically inspired signal-processing tools.
WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind.The technique, outlined in a paper in September 2016, [1] is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method trained with recordings of real speech.
First, compute HRTFs for the 3D sound localization, by formulating two equations; one represents the signal of a given sound source and the other indicates the signal output from the robot head microphones for the sound transferred from the source. Monaural input data are processed by these HRTFs, and the results are output from stereo headphones.