Music understanding is a process closely related to the knowledge and experience of the listener. The amount of knowledge required is relative to the complexity of the task in hand.|
This dissertation is concerned with the problem of automatically decomposing musical signals into a score-like representation. It proposes that, as with humans, an automatic system requires knowledge about the signal and its expected behaviour to correctly analyse music.
The proposed system uses the blackboard architecture to combine the use of knowledge with data provided by the bottom-up processing of the signal's information. Methods are proposed for the estimation of pitches, onset times and durations of notes in simple polyphonic music. A method for onset detection is presented. It provides an alternative to conventional energy-based algorithms by using phase information. Statistical analysis is used to create a detection function that evaluates the expected behaviour of the signal regarding onsets.
Two methods for multi-pitch estimation are introduced. The first concentrates on the grouping of harmonic information in the frequency-domain. Its performance and limitations emphasise the case for the use of high-level knowledge.
This knowledge, in the form of the individual waveforms of a single instrument, is used in the second proposed approach. The method is based on a time-domain linear additive model and it presents an alternative to common frequency-domain approaches.
Results are presented and discussed for all methods, showing that, if reliably generated, the use of knowledge can significantly improve the quality of the analysis.