Name
Abstract | ||
|---|---|---|
A good representation of the audio is important for music genre classification. Deep neural networks (DNN) enable a better approach to learn the representation of audio. The representation learned from DNN, which is known as bottleneck feature, is widely used for speech and audio related application. However, in general, it needs a large amount of transcribed data to learn an effective bottleneck feature extractor. While, in reality, the amount of transcribed data is often limited. In this paper, we investigate a semi-supervised learning to train the bottleneck feature for music data. Then, the bottleneck feature is used for music genre classification. Since the target dataset contains few data, which cannot be used train a reliable bottleneck DNN, we train the DNN bottleneck extractor on a large out-of-domain un-transcribed dataset in semi-supervised way. Experimental results show that with the learned bottleneck feature, the proposed system can perform better than the state-of-the-art best methods on GTZAN dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3005-5_45 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Bottleneck,DNN,Semi-supervised,Multilingual,Cross-lingual | Bottleneck,Cross lingual,Semi-supervised learning,Computer science,Extractor,Artificial intelligence,Machine learning,Deep neural networks | Conference |
Volume | ISSN | Citations |
663 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |