A multi-timbre chord/harmony analyzer based on signal processing and neural networks

The automatic analysis of a polyphonic sound is still a very challenging task, not only for computational reasons but also because of the lack in suitable techniques and restrictions in the application field, or sometimes due to unrealistic goals. A remarkable progress has been made in the last decade, but still, a practical and generic solution for this problem is hard to find. In this paper, we propose a rather general solution for a chord/harmony analyzer, which is able provide good results for different instruments and polyphonic sounds. It is based on the combination of signal processing and neural networks. The sound is analyzed in both time and frequency domains by a hybrid original technique. A rather innovative approach is then used to classify the chords and extract their evolution time. The proposed overall method aims to implement a general purpose listening machine, whose approximated results and approach are nevertheless general enough to allow the implementation of very useful applications.