Semantic Scholar Open Access 2016 525 sitasi

Convolutional recurrent neural networks for music classification

Keunwoo Choi György Fazekas Mark B. Sandler Kyunghyun Cho

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Abstrak

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.

Topik & Kata Kunci

Computer Science

Penulis (4)

Keunwoo Choi

György Fazekas

Mark B. Sandler

Kyunghyun Cho

Format Sitasi

APA MLA BibTeX

Choi, K., Fazekas, G., Sandler, M.B., Cho, K. (2016). Convolutional recurrent neural networks for music classification. https://doi.org/10.1109/ICASSP.2017.7952585

Akses Cepat

Lihat di Sumber doi.org/10.1109/ICASSP.2017.7952585

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 525×
Sumber Database: Semantic Scholar
DOI: 10.1109/ICASSP.2017.7952585
Akses: Open Access ✓