On the relevance of spectral features for instrument classification |
| | Abstract | Automatic knowledge extraction from music signals is a key
component for most music organization and music information
retrieval systems. In this paper, we consider the problem
of instrument modelling and instrument classification from
the rough audio data. Existing systems for automatic instrument
classification operate normally on a relatively large
number of features, from which those related to the spectrum
of the audio signal are particularly relevant. In this
paper, we confront two different models about the spectral
characterization of musical instruments. The first assumes
a constant envelope of the spectrum (i.e., independent from
the pitch), whereas the second assumes a constant relation
among the amplitude of the harmonics. The first model is related
to the Mel Frequency Cepstrum Coefficients (MFCCs),
while the second leads to what we will refer to as Harmonic
Representation (HR). Experiments on a large database of real
instrument recordings show that the first model offers a more
satisfactory characterization, and therefore MFCCs should be
preferred to HR for instrument modelling/classification. | | Keywords | musical instrument modelling, harmonics structure, feature extraction | | Type | Conference paper [With referee] | | Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing | | Year | 2007 Month April | | Publisher | Honolulu, Hawaii | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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