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In this paper, we address the problem of automatically recognising and identifying an instrument from a set of solo recordings. A system using the LSF as features whose statistical properties are learnt using the k-means algorithm is described. During the training phase, models are built by determining an optimised codebook of LSF vectors for each class of instruments. During the identification phase, one codebook is similarly extracted from the unknown audio sample. A distortion measure between two codebooks is then used to retrieve the identity of the presented excerpt. System performances are evaluated using a database of 11 instruments.
Author (s): Chetry, Nicolas;
Davies, Mike;
Sandler, Mark;
Affiliation:
Queen Mary University
(See document for exact affiliation information.)
AES Convention: 118
Paper Number:6413
Publication Date:
2005-05-06
Session subject:
Analysis and Synthesis of Sound
DOI:
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Chetry, Nicolas; Davies, Mike; Sandler, Mark; 2005; Musical Instrument Identification using LSF and K-means [PDF]; Queen Mary University; Paper 6413; Available from: https://aes.org/publications/elibrary-page/?id=13129
Chetry, Nicolas; Davies, Mike; Sandler, Mark; Musical Instrument Identification using LSF and K-means [PDF]; Queen Mary University; Paper 6413; 2005 Available: https://aes.org/publications/elibrary-page/?id=13129
@inproceedings{Chetry2005musical,
title={{Musical Instrument Identification using LSF and K-means}},
author={Chetry, Nicolas and Davies, Mike and Sandler, Mark},
year={2005},
month={may},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 6413; AES Convention 118; May 2005},
number={6413},
organization={AES},
}
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