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 of Musical Sources using Pattern Recognition / Costantini, G.; Antici, P.; Panella, Massimo; FRATTALE MASCIOLI, Fabio Massimo. - ELETTRONICO. - CD-ROM:(2000), pp. 1-5. (Intervento presentato al convegno Engineering of Intelligent Systems tenutosi a Paisley, Scozia (Gran Bretagna) nel 27-30 giugno 2000).

Nonexclusive Classification of Musical Sources using Pattern Recognition

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.
2000
3906454215
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/365983
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