| Paper: | PS-2B.32 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1153-0 |
| Authors: |
Mingwen Dong, Rutgers University, United States |
| Abstract: |
Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. Here, we propose a method that achieves human-level accuracy (70%) in the same classification task. The method is inspired by knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. It works by training a simple convolutional neural network (CNN) to classify short segments of music waveforms. During prediction, the genre of an unknown music is determined as the majority vote of all classified segments from a music waveform. The filters learned in the CNN qualitatively resemble the spectro-temporal receptive fields (STRF) in the auditory system and potentially provide insights about how human auditory system classifies music genre. |
