Research on Music Classification Based on MFCC and BP Neural Network
LiuYong Chun, Hong Song, Jing Yang
Available Online March 2014.
- https://doi.org/10.2991/icieac-14.2014.29How to use a DOI?
- BP neural network; MFCC feature extraction; music classification; hidden Markov model
- Because of the diversity and uncertainty of music, the classification rate and accuracy are both lower for the traditional classification methods in the large-scale music classification application. A based on BP neural network (BPNN) music classification method proposed in this paper can improve this performance, which extracts the feature parameters of music through mel frequency cepstrum coefficient(MFCC) firstly, and then the BPNN is used to train feature signals and establish the optimal classifier model, finally classifies the test music dataset. The average classification accuracy rate is up to 90.2%, and higher 7% than the HMM classification method by simulation experiments for the folk, classical, rock and pop different types of music, therefore, the results show that the BPNN is a quite effective music type classification method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - LiuYong Chun AU - Hong Song AU - Jing Yang PY - 2014/03 DA - 2014/03 TI - Research on Music Classification Based on MFCC and BP Neural Network BT - Proceedings of the 2nd International Conference on Information, Electronics and Computer PB - Atlantis Press SP - 129 EP - 132 SN - 1951-6851 UR - https://doi.org/10.2991/icieac-14.2014.29 DO - https://doi.org/10.2991/icieac-14.2014.29 ID - Chun2014/03 ER -