Classification of Musical Genres Using Audio Waveform Descriptors in MPEG-7
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N. Osmanovic, "Classification of Musical Genres Using Audio Waveform Descriptors in MPEG-7," Paper 7513, (2008 October.). doi:
N. Osmanovic, "Classification of Musical Genres Using Audio Waveform Descriptors in MPEG-7," Paper 7513, (2008 October.). doi:
Abstract: Automated genre classification makes it possible to determine the musical genre of an incoming audio waveform. One application of this is to help listeners find music they like more quickly among millions of tracks in an online music store. By using numerical thresholds and the MPEG-7 descriptors, a computer can analyze the audio stream for occurrences of specific sound events such as kick drum, snare hit, and guitar strum. The knowledge about sound events provides a basis for the implementation of a digital music genre classifier. The classifier inputs a new audio file, extracts salient features, and makes a decision about the musical genre based on the decision rule. The final classification results show a recognition rate in the range 75% - 94% for five genres of music
@article{osmanovic2008classification,
author={osmanovic, nermin},
journal={journal of the audio engineering society},
title={classification of musical genres using audio waveform descriptors in mpeg-7},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{osmanovic2008classification,
author={osmanovic, nermin},
journal={journal of the audio engineering society},
title={classification of musical genres using audio waveform descriptors in mpeg-7},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={automated genre classification makes it possible to determine the musical genre of an incoming audio waveform. one application of this is to help listeners find music they like more quickly among millions of tracks in an online music store. by using numerical thresholds and the mpeg-7 descriptors, a computer can analyze the audio stream for occurrences of specific sound events such as kick drum, snare hit, and guitar strum. the knowledge about sound events provides a basis for the implementation of a digital music genre classifier. the classifier inputs a new audio file, extracts salient features, and makes a decision about the musical genre based on the decision rule. the final classification results show a recognition rate in the range 75% - 94% for five genres of music},}
TY - paper
TI - Classification of Musical Genres Using Audio Waveform Descriptors in MPEG-7
SP -
EP -
AU - Osmanovic, Nermin
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008
TY - paper
TI - Classification of Musical Genres Using Audio Waveform Descriptors in MPEG-7
SP -
EP -
AU - Osmanovic, Nermin
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008
AB - Automated genre classification makes it possible to determine the musical genre of an incoming audio waveform. One application of this is to help listeners find music they like more quickly among millions of tracks in an online music store. By using numerical thresholds and the MPEG-7 descriptors, a computer can analyze the audio stream for occurrences of specific sound events such as kick drum, snare hit, and guitar strum. The knowledge about sound events provides a basis for the implementation of a digital music genre classifier. The classifier inputs a new audio file, extracts salient features, and makes a decision about the musical genre based on the decision rule. The final classification results show a recognition rate in the range 75% - 94% for five genres of music
Automated genre classification makes it possible to determine the musical genre of an incoming audio waveform. One application of this is to help listeners find music they like more quickly among millions of tracks in an online music store. By using numerical thresholds and the MPEG-7 descriptors, a computer can analyze the audio stream for occurrences of specific sound events such as kick drum, snare hit, and guitar strum. The knowledge about sound events provides a basis for the implementation of a digital music genre classifier. The classifier inputs a new audio file, extracts salient features, and makes a decision about the musical genre based on the decision rule. The final classification results show a recognition rate in the range 75% - 94% for five genres of music
Author:
Osmanovic, Nermin
Affiliation:
Microsoft Corporation
AES Convention:
125 (October 2008)
Paper Number:
7513
Publication Date:
October 1, 2008Import into BibTeX
Subject:
Analysis and Synthesis of Sound
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=14665