Abstract
Music emotion classification is one of the most importance parts of music information retrieval (MIR) because of its potential commercial value and cultural value. However, music emotion classification is still a tough challenge, due to the low representation of music features. In this paper, a novel Extreme Learning Machine (ELM), combining graph regularization term and multiple kernel, is proposed to enhance the accuracy of music emotion classification. We use nonnegative matrix factorization (NMF) to find the optimal weights of combining multiple kernels. Furthermore, the graph regularization term is added to increase the relevance between predictions from the same class. The proposed Graph embedded Multiple Kernel Extreme Learning Machine (GMK-ELM) is tested on three music emotion datasets. Experiment results show that the proposed GMK-ELM outperforms several well-known ELM methods.
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Katayose, H., Imai, M., Inokuchi, S.: Sentiment extraction in music. In: 9th International Conference on Pattern Recognition, pp. 1083–1087. IEEE (1988)
Feng, Y., Zhuang, Y., Pan, Y.: Popular music retrieval by detecting mood. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 375–376. ACM (2003)
Ren, J., Wu, M., Jang, J.S.R.: Automatic music mood classification based on timbre and modulation features. IEEE Trans. Affect. Comput. 6(3), 236–246 (2015)
Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A.: Music classification using extreme learning machines. In: 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 377–381. IEEE (2013)
Zhang, Y., et al.: Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst. Appl. 96, 302–310 (2018)
Ergul, U., Bilgin, G.: MCK-ELM: multiple composite kernel extreme learning machine for hyperspectral images. Neural Comput. Appl. 1–11 (2019)
Yang, Z., Cao, F., Zabalza, J., Chen, W., Cao, J.: Spectral and spatial kernel extreme learning machine for hyperspectral image classification. In: Ren, J., et al. (eds.) BICS 2018. LNCS (LNAI), vol. 10989, pp. 394–401. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00563-4_38
Gu, Y., Wang, Q., Wang, H., You, D., Zhang, Y.: Multiple kernel learning via low-rank nonnegative matrix factorization for classification of hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2739–2751 (2015)
Iosifidis, A., Tefas, A., Pitas, I.: Graph embedded extreme learning machine. IEEE Trans. Cybern. 46(1), 311–324 (2016)
Huang, G., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. 42(2), 513–529 (2012)
Gu, Y., Chanussot, J., Jia, X., Benediktsson, J.A.: Multiple kernel learning for hyperspectral image classification: a review. IEEE Trans. Geosci. Remote Sens. 55(11), 6547–6565 (2017)
Lee, D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)
Peng, Y., Wang, S., Long, X., Lu, B.L.: Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149, 340–353 (2015)
Zhu, M., Martinez, A.M.: Subclass discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1274–1286 (2006)
Sukittanon, S., Atlas, L.E., Pitton, J.W.: Modulation-scale analysis for content identification. IEEE Trans. Signal Process. 52(10), 3023–3035 (2004)
Lee, C.H., Shih, J.L., Yu, K.M., Lin, H.S.: Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features. IEEE Trans. Multimedia 11(4), 670–682 (2009)
Eerola, T., Vuoskoski, J.K.: A comparison of the discrete and dimensional models of emotion in music. Psychol. Music 39(1), 18–49 (2011)
Song, Y., Dixon, S., Pearce, M.: Evaluation of musical features for emotion classification. In: 13th International Society for Music Information Retrieval Conference, ISMIR, pp. 523–528 (2012)
Cao, F., Yang, Z., Ren, J., Ling, W.K., Zhao, H., Sun, M., et al.: Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 56(11), 6263–6279 (2018)
Cao, F., Yang, Z., Ren, J., Ling, W.K.: Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. Remote Sens. 9(12), 1255 (2017)
Fang, L., Li, S., Duan, W., Ren, J., Benediktsson, J.A.: Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels. IEEE Trans. Geosci. Remote Sens. 53(12), 6663–6674 (2015)
Feng, W., Huang, W., Ren, J.: Class imbalance ensemble learning based on the margin theory. Appl. Sci. 8(5), 815 (2018)
Acknowledgement
This work is supported in part by the National Nature Science Foundation of China (nos. U1701266, 61471132), the Innovation Team Project of Guangdong Education Department (no. 2017KCXTD011), Natural Science Foundation of Guangdong Province China (no. 2018A030313751), and Science and Technology Program of Guangzhou, China (nos. 201803010065, 201802020010).
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Zhang, X., Yang, Z., Ren, J., Wang, M., Ling, WK. (2020). Graph Embedded Multiple Kernel Extreme Learning Machine for Music Emotion Classification. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_17
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DOI: https://doi.org/10.1007/978-3-030-39431-8_17
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