T. Manjunath, J. K. Pawani, and A. Lerch, “Automatic Classification of Live and Studio Audio Recordings,” in Proc. AES Convention 149, Oct. 2020, Paper 10399. [Online]. Available: https://aes.org/publications/elibrary-page/?id=20936
Manjunath T, Pawani JK, Lerch A. Automatic Classification of Live and Studio Audio Recordings. In: AES Convention 149. Audio Engineering Society; 2020. Paper 10399. Available from: https://aes.org/publications/elibrary-page/?id=20936
@inproceedings{Manjunath2020_20936,
author = {Manjunath, Tejas and Pawani, Jeet Kiran and Lerch, Alexander},
title = {{Automatic Classification of Live and Studio Audio Recordings}},
booktitle = {AES Convention 149},
note = {Paper 10399},
year = {2020},
month = oct,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=20936}
}
TY - CPAPER
TI - Automatic Classification of Live and Studio Audio Recordings
AU - Manjunath, Tejas
AU - Pawani, Jeet Kiran
AU - Lerch, Alexander
T2 - AES Convention 149
M1 - Paper 10399
PY - 2020
DA - 2020/10/06
UR - https://aes.org/publications/elibrary-page/?id=20936
PB - Audio Engineering Society
LA - en
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.
ER -
