Pattern recognition algorithms for polyphonic music transcription
The main area of work in computer music related to information systems is known as music information retrieval (MIR). Databases containing musical information can be classified into two main groups: those containing audio data (digitized music) and those that file symbolic data (digital music scores). The latter are much more abstract that the former ones and contain a lot of information already coded in terms of musical symbols, thus MIR algorithms are easier and more efficient when dealing with symbolic databases. The automatic extraction of the notes in a digital musical signal (automatic music transcription) permits applying symbolic processing algorithms to audio data. In this work the performance of a neural approach and a well known non parametric algorithm, like nearest neighbours, when dealing with this problem using spectral pattern identification is analyzed.