Title | ||
---|---|---|
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification. |
Abstract | ||
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Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors. In particular, the feature extractors are trained in sample-level deep convolutional neural networks using raw waveforms. We show that this approach achieves state-of-the-art results on several music classification datasets. |
Year | Venue | Field |
---|---|---|
2017 | arXiv: Sound | Abstraction,Pattern recognition,Convolutional neural network,Computer science,Speech recognition,Artificial intelligence,Deep learning,Feature aggregation,Machine learning |
DocType | Volume | Citations |
Journal | abs/1706.06810 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jongpil Lee | 1 | 111 | 15.79 |
Juhan Nam | 2 | 261 | 25.12 |