The Use of Artificial Neural Networks in the Estimation of the Perception of Sound by the Human Auditory System

The human auditory system perceives sound in a much different manner than how sound is measured by modern audio sensing systems. The most commonly referenced aspects of auditory perception are loudness and pitch, which are related to the objective measures of audio signal frequency and sound pressure level. Here we describe an efficient and accurate method for the conversion of the sensed factors of frequency and sound pressure level to perceived loudness and pitch. This method is achieved through the modeling of the physical auditory system and the biological neural network of the primary auditory cortex using artificial neural networks. The behavior of artificial neural networks both during and after the training process has also been found to mimic that of biological neural networks and this method will be shown to have certain advantages over previous methods in the modeling of auditory perception. This work will describe the nature of artificial neural networks and investigate their suitability over other modeling methods for the task of perception modeling, taking into account development and implementation complexity. It will be shown that while known points on the perception scales of loudness and pitch can be used to objectively test the suitability of artificial neural networks, it is in the estimation of the perception of sound from the unknown (or unseen) data points that this method excels.