Abstract: There are disclosed methods and apparatus for understanding music. A classifier machine may be trained for each of a plurality of selected terms using a first plurality of music samples. The classifier machines may then be tested using a second plurality of music samples. The results from testing the classifier machines may then be used to select a plurality of semantic basis function from the selected terms. A semantic basis classifier machine may then be trained for each semantic basis function.

Abstract: The method of the present invention utilizes machine-learning techniques, particularly Support Vector Machines in combination with a neural network, to process a unique machine-learning enabled representation of the audio bitstream. Using this method, a classifying machine is able to autonomously detect characteristics of a piece of music, such as the artist or genre, and classify it accordingly. The method includes transforming digital time-domain representation of music into a frequency-domain representation, then dividing that frequency data into time slices, and compressing it into frequency bands to form multiple learning representations of each song. The learning representations that result are processed by a group of Support Vector Machines, then by a neural network, both previously trained to distinguish among a given set of characteristics, to determine the classification.