Genre Recognition from Symbolic Music with CNNs

Author:

Dervakos Edmund,Kotsani Natalia,Stamou Giorgos

Publisher

Springer International Publishing

Reference31 articles.

1. Dong, H.-W., Hsiao, W.-Y., Yang, L.-C., Yang, Y.-H.: MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

2. Mao, H.H., Shin, T., Cottrell, G.: DeepJ: style-specific music generation. In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), pp. 377–382. IEEE (2018)

3. Brunner, G., Konrad, A., Wang, Y., Wattenhofer, R.: Midi-VAE: modeling dynamics and instrumentation of music with applications to style transfer. In: 19th International Society for Music Information Retrieval Conference (ISMIR 2018) (2018)

4. Oramas, S., Barbieri, F., Nieto, O., Serra, X.: Multimodal deep learning for music genre classification. Trans. Int. Soc. Music Inf. Retrieval 1(1), 4–21 (2018)

5. Yu, Y., Luo, S., Liu, S., Qiao, H., Liu, Y., Feng, L.: Deep attention based music genre classification. Neurocomputing 372, 84–91 (2020)

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