Audio is a difficult medium to analyze. Compared with text, audio has more noise. There can be multiple people speaking at the same time, more kinds of information, and the listener may be interested in any combination of these. Different AI models have been developed to handle different aspects of audio processing. Convolutional neural networks have been used for isolating voices6, and transformer networks have been used for generating audio. These architectures come from image and text processing. Considering the unique challenges of audio, we may be able to improve audio-models by taking inspiration from the brain’s auditory system. We have seen that the audio system has multiple branches: language and music processing. Since music is a purely auditory phenomena, the music perception system may hold some undiscovered gems for audio-models. One potential inspiration is beat synchronization. When we listen to music, we often nod our head or tap our fingers to the beat. This synchronization is anticipatory. We must predict the next beat in order to synchronize our movements with it. Predictive coding has been suggested as the underlying mechanism.

              Predictive coding is a general theory of brain function. The idea is that the brain is always trying to predict its own future state. The resulting prediction error is used to update the brain’s predictive model. These two steps, prediction and error update, are very familiar to anyone who has trained a neural network with backpropagation. One difference is that back propagation separates the training and operation of a model, while predictive coding supposes that training is integrated into a model’s operation. Integrating training and operation would allow models to continue learning after deployment. When we release AI models into the real world, they will come across situations that we have never trained them for. If models were able to improve in-situ, they might learn to solve these novel problems. Reconsider the constants of the human condition: conflict, compassion, and music. Each of these present challenges that evolve in real time. Conflict and compassion require updating predictive models of yourself and other people. Music perception creates and updates a predictive model of sound. The similarities are clear. Perhaps someday AI models designed to process music will help us understand other aspects of the human condition.