Automatic Classification of Live and Studio Audio Recordings
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T. Manjunath, JE. KI. Pawani, and A. Lerch, "Automatic Classification of Live and Studio Audio Recordings," Paper 10399, (2020 October.). doi:
T. Manjunath, JE. KI. Pawani, and A. Lerch, "Automatic Classification of Live and Studio Audio Recordings," Paper 10399, (2020 October.). doi:
Abstract: We present a study on the automatic classification of live and studio audio recordings, an important meta-information for music catalogue browsing and music recommendation systems. Several possible input representations (MFCCs, Mel spectrograms, VGGish) are combined with the classifiers GMM, SVM, and CNN to identify the most powerful approach. The results show that a CNN with VGGish input clearly outperforms other approaches and that its detection accuracy is high enough to be useful in practical applications.
@article{manjunath2020automatic,
author={manjunath, tejas and pawani, jeet kiran and lerch, alexander},
journal={journal of the audio engineering society},
title={automatic classification of live and studio audio recordings},
year={2020},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{manjunath2020automatic,
author={manjunath, tejas and pawani, jeet kiran and lerch, alexander},
journal={journal of the audio engineering society},
title={automatic classification of live and studio audio recordings},
year={2020},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={we present a study on the automatic classification of live and studio audio recordings, an important meta-information for music catalogue browsing and music recommendation systems. several possible input representations (mfccs, mel spectrograms, vggish) are combined with the classifiers gmm, svm, and cnn to identify the most powerful approach. the results show that a cnn with vggish input clearly outperforms other approaches and that its detection accuracy is high enough to be useful in practical applications.},}
TY - paper
TI - Automatic Classification of Live and Studio Audio Recordings
SP -
EP -
AU - Manjunath, Tejas
AU - Pawani, Jeet Kiran
AU - Lerch, Alexander
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2020
TY - paper
TI - Automatic Classification of Live and Studio Audio Recordings
SP -
EP -
AU - Manjunath, Tejas
AU - Pawani, Jeet Kiran
AU - Lerch, Alexander
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2020
AB - We present a study on the automatic classification of live and studio audio recordings, an important meta-information for music catalogue browsing and music recommendation systems. Several possible input representations (MFCCs, Mel spectrograms, VGGish) are combined with the classifiers GMM, SVM, and CNN to identify the most powerful approach. The results show that a CNN with VGGish input clearly outperforms other approaches and that its detection accuracy is high enough to be useful in practical applications.
We present a study on the automatic classification of live and studio audio recordings, an important meta-information for music catalogue browsing and music recommendation systems. Several possible input representations (MFCCs, Mel spectrograms, VGGish) are combined with the classifiers GMM, SVM, and CNN to identify the most powerful approach. The results show that a CNN with VGGish input clearly outperforms other approaches and that its detection accuracy is high enough to be useful in practical applications.
Authors:
Manjunath, Tejas; Pawani, Jeet Kiran; Lerch, Alexander
Affiliation:
Georgia Institute of Technology, Atlanta, GA, USA
AES Convention:
149 (October 2020)
Paper Number:
10399
Publication Date:
October 22, 2020Import into BibTeX
Subject:
Audio Processing
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=20936
