2019

Bottom-up Broadcast Neural Network For Music Genre Classification

Liu, Caifeng, Feng, Lin, Liu, Guochao et al.

Understand

Music genre recognition based on visual representation has been successfully explored over the last years.

Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task.
However, most of existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification.
Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audios and develop a novel CNN architecture in this paper.

Built on

Musical genre classification of audio signals
Tzanetakis, G., Cook, P., 2002 · 2002
Earlier work this paper cites.
Ismir 2004 audio description contest
Cano, P., Gómez Gutiérrez, E., Gouyon, F., Herrera Boyer, P., Koppenberger, M., Ong, B.S., Serra, X., Streich, S., Wack, N., 2006 · 2004
Earlier work this paper cites.
The million song dataset., in: Ismir, p. 10
Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P., 2011 · 2011
Earlier work this paper cites.
A survey of audio-based music classification and annotation
Fu, Z., Lu, G., Ting, K.M., Zhang, D., 2011 · 2011
Earlier work this paper cites.
Network in network
Original
Lin, M., Chen, Q., Yan, S., 2013 · 2013
Earlier work this paper cites.
Forest species recognition using deep convolutional neural networks, in: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE. pp. 1103–1107
Hafemann, L.G., Oliveira, L.S., Cavalin, P., 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
The modulation scale spectrum and its application to rhythm-content analysis, in: DAFX (Digital Audio Effects)
Marchand, U., Peeters, G., 2014 · 2014
Earlier work this paper cites.
"multilingual" deep neural network for music genre classification, in: Sixteenth Annual Conference of the International Speech Communication Association
Dai, J., Liu, W., Ni, C., Dong, L., Yang, H., 2015 · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
Ioffe, S., Szegedy, C., 2015 · 2015
Earlier work this paper cites.
Beat histogram features for rhythm-based musical genre classification using multiple novelty functions, in: Proceedings of the 16th ISMIR Conference, pp. 434–440
Lykartsis, A., Lerch, A., 2015 · 2015
Earlier work this paper cites.

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librosa: Audio and music signal analysis in python, in: Proceedings of the 14th python in science conference, pp. 18–25
McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., Nieto, O., 2015 · 2015
Cited alongside, same era.
Going deeper with convolutions, in: IEEE conference on computer vision and pattern recognition, pp. 1–9
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015 · 2015
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Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q., Huang, X., 2015 · 2015
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Scale and shift invariant time/frequency representation using auditory statistics: Application to rhythm description, in: Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, IEEE. pp. 1–6
Marchand, U., Peeters, G., 2016b · 2016
Cited alongside, same era.
Experimenting with musically motivated convolutional neural networks, in: Content-Based Multimedia Indexing (CBMI), 2016 14th International Workshop on, IEEE. pp. 1–6
Pons, J., Lidy, T., Serra, X., 2016 · 2016
Cited alongside, same era.
Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering, in: International Joint Conference on Artificial Intelligence, pp. 2153–2159
Wang, Y., Zhang, W., Wu, L., Lin, X., Fang, M., Pan, S., 2016 · 2016
Cited alongside, same era.
Improved music genre classification with convolutional neural networks., in: INTERSPEECH, pp. 3304–3308
Zhang, W., Lei, W., Xu, X., Xing, X., 2016 · 2016
Cited alongside, same era.
Transfer learning for music classification and regression tasks
Original
Choi, K., Fazekas, G., Sandler, M., Cho, K., 2017 · 2017
Cited alongside, same era.
audeep: Unsupervised learning of representations from audio with deep recurrent neural networks
Freitag, M., Amiriparian, S., Pugachevskiy, S., Cummins, N., Schuller, B., 2017 · 2017
Cited alongside, same era.
Evaluation of gated recurrent neural networks in music classification tasks, in: International Conference on Information Systems Architecture and Technology, Springer. pp. 27–37
Jakubik, J., 2017 · 2017
Cited alongside, same era.
Dlr:toward a deep learned rhythmic representation for music content analysis
Original
Jeong, Y., Choi, K., Jeong, H., 2017 · 2017
Cited alongside, same era.
The extended ballroom dataset
Marchand, U., Peeters, G., 2016a
Cited in the paper.

Then

Automatic classification of music genre using masked conditional neural networks, in: Data Mining (ICDM), 2017 IEEE International Conference on, IEEE. pp. 979–984
Medhat, F., Chesmore, D., Robinson, J., 2017 · 2017
Later among the works it cites.
Combining visual and acoustic features for audio classification tasks
Nanni, L., Costa, Y.M., Lucio, D.R., Silla Jr, C.N., Brahnam, S., 2017 · 2017
Later among the works it cites.
Designing efficient architectures for modeling temporal features with convolutional neural networks, in: Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, IEEE. pp. 2472–2476
Pons, J., Serra, X., 2017 · 2017
Later among the works it cites.
Deep convolutional neural networks and data augmentation for environmental sound classification
Salamon, J., Bello, J.P., 2017 · 2017
Later among the works it cites.
Music feature maps with convolutional neural networks for music genre classification, in: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, ACM. p. 19
Senac, C., Pellegrini, T., Mouret, F., Pinquier, J., 2017 · 2017
Later among the works it cites.
A scalable hybrid classifier for music genre classification using machine learning concepts and spark, in: 2018 International Conference on Intelligent Autonomous Systems (ICoIAS), IEEE. pp. 128–135
Karunakaran, N., Arya, A., 2018 · 2018
Later among the works it cites.
Randomly weighted cnns for (music) audio classification
Original
Pons, J., Serra, X., 2018 · 2018
Later among the works it cites.
Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering
Wang, Y., Wu, L., 2018 · 2018
Later among the works it cites.
Multiview spectral clustering via structured low-rank matrix factorization
Wang, Y., Wu, L., Lin, X., Gao, J., 2018 · 2018
Later among the works it cites.
Deep attention-based spatially recursive networks for fine-grained visual recognition
Wu, L., Wang, Y., Li, X., Gao, J., 2018 · 2018
Later among the works it cites.
Cycle-consistent deep generative hashing for cross-modal retrieval
Wu, L., Wang, Y., Shao, L., 2019 · 2019
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