Transfer Learning for Music Classification and Regression Tasks
Abstrak
In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.
Topik & Kata Kunci
Penulis (4)
Keunwoo Choi
György Fazekas
Mark B. Sandler
Kyunghyun Cho
Akses Cepat
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- 2017
- Bahasa
- en
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