Method of training a neural network to reflect emotional perception and related system and method for categorizing and finding associated content

摘要

Training an artificial intelligence to reflect human subjective responses to stimuli, e.g. audio, music, images, videos or text, employs an artificial neural network (ANN) to identify similarity between the content of two files, e.g. music tracks. For audio files, measurable signal qualities, e.g. rhythm, tonality, timbre and texture, are extracted from both files to identify musical properties 404. The ANN outputs a property vector for each musical property 408, and the property vectors are assembled into a multi-dimensional vector for each file. The weights or bias values of the ANN are adjusted by backpropagation 422 based on the distance between the two multi-dimensional vectors 410 and any discrepancy between this distance and a quantified semantic dissimilarity distance between the two files in semantic space 412, such as textual description of the track or other semantic associations with events, feelings, themes or environments. Thus, the ANN is trained to align the distance between the files in property space to correspond to the distance between the files in semantic space. The ANN can then identify music files which are subjectively semantically similar to a target file based only on the target file’s objective musical properties.