Recognition of musical instruments by statistical classification

The correct classification of single musical sources is a relevant aspect for the source separation task and the automatic transcription of polyphonic music. In this paper, we present a classification experiment on six different musical instruments: violin, clarinet, flute, oboe, saxophone and piano. It is characterized by two steps. In the first step, a suitable signal preprocessing based on FFT and QFT (Q-constant Frequency Transform) is adopted for feature extraction and data set preparation. In the second step, a nonexclusive classification method is proposed to handle the inevitable overlapping among classes. It is obtained by a co-operative clustering technique. The success of this kind of classification method is conditioned by the adopted clustering procedure. We propose a hierarchical scale-based approach for this task, carrying out good results.