TY - GEN
T1 - Music era classification using hierarchical-level fusion
AU - Pratama, M. Octaviano
AU - Adriani, Mima
N1 - Funding Information:
This work is supported by Hibah PITTA 2018 funded by DRPM Universitas Indonesia No. 1884/ UN2.R3.1/ HKP.05.00/ 2018.
Publisher Copyright:
© 2019 IEEE. All Rights Reserved.
PY - 2019/1/17
Y1 - 2019/1/17
N2 - Music era is one of Music Information Retrieval research that connecting several songs with similar characteristics from similar year or decade but not limited to particular genre and mood. Previous researcher tried to recognize musical era with classification model using single audio feature like spectrogram and chromagram, but the performance was poor. Feature and model selection affect classification era performance. One of the challenge in selecting feature is whether the using of multimodal or combination of audio features can improve music era classification performance. In this research, Hierarchical-level fusion model is used to combine several audio features like spectrogram and chromagram to determine music era. We obtained both 83% and 73% overall accuracy for Indonesian Music Dataset (IMD) and Million Song Dataset (MSD) of era classification tasks using Hierarchical-level fusion model. This research result also strengthened with overall precision, recall, and F-score result 0.83,0.82, 0.82 for IMD dataset and 0.73, 0.72, 0.72 for MSD dataset experiment.
AB - Music era is one of Music Information Retrieval research that connecting several songs with similar characteristics from similar year or decade but not limited to particular genre and mood. Previous researcher tried to recognize musical era with classification model using single audio feature like spectrogram and chromagram, but the performance was poor. Feature and model selection affect classification era performance. One of the challenge in selecting feature is whether the using of multimodal or combination of audio features can improve music era classification performance. In this research, Hierarchical-level fusion model is used to combine several audio features like spectrogram and chromagram to determine music era. We obtained both 83% and 73% overall accuracy for Indonesian Music Dataset (IMD) and Million Song Dataset (MSD) of era classification tasks using Hierarchical-level fusion model. This research result also strengthened with overall precision, recall, and F-score result 0.83,0.82, 0.82 for IMD dataset and 0.73, 0.72, 0.72 for MSD dataset experiment.
KW - Audio Feature
KW - Hierarchical-level fusion
KW - Music era
UR - http://www.scopus.com/inward/record.url?scp=85062395609&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2018.8618242
DO - 10.1109/ICACSIS.2018.8618242
M3 - Conference contribution
AN - SCOPUS:85062395609
T3 - 2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
SP - 331
EP - 335
BT - 2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
Y2 - 27 October 2018 through 28 October 2018
ER -