A System for the Automatic Segmentation and Classification of Chord Sequences

Automatic chord recognition has been a topic of interest in the context of Music Information Retrieval (MIR) for several years, and attempts have been made at implementing such systems using well understood Signal Processing and Pattern Recognition techniques. The sequence of chords in a musical recording, in addition to providing the melody, often provides the most effective way to summarize a piece of music. Information on the chord structure of a musical work is also useful in the context of Interactive Music Systems. In nearly every previous chord recognition system, a summation of filterbank outputs, called the Pitch Class Profile (PCP), (which is a measure of the spectral energy in different frequency regions), has been used to represent the signal. Because spectral characteristics of the musical sounds vary between different sound sources,spectral energy measurements (if not properly preprocessed)will capture information inherent to a particular instrument.In this paper we give experimental results that show the benefits of proper signal processing before calculating the spectral energy via the PCP (or any related measure). Also,we discuss a method for finding the boundaries between different chord labels by suitably combining multiple measures of audio novelty. Together, the segmentation and classification methods that we have developed have enabled high recognition rates for musical chords.