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.
Nonexclusive classification and recognition of traditional musical instruments / Costantini, G; Antici, P; Panella, Massimo; FRATTALE MASCIOLI, Fabio Massimo. - STAMPA. - (2000), pp. 119-122. (Intervento presentato al convegno XIII Colloquium on Musical Informatics tenutosi a L’Aquila, Italia nel 2-5 settembre 2000).
Nonexclusive classification and recognition of traditional musical instruments
PANELLA, Massimo;FRATTALE MASCIOLI, Fabio Massimo
2000
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.