Abstract
The precise prediction of the popular trend in music can contribute to the exploration of the potential entertainment market. According to surveys, the technical difficulties of such prediction contain the difference between computer simulation and the real human emotions, as well as the comprehensive factors and data that are processed. Therefore, this thesis will present SANet which can forecast the popular trend in songs by self-accommodating and nonlinear mapping. It will be demonstrated by focusing on the discussion in the areas on data preprocessing, model constructing, and accommodating of hidden columns, as well as the test of partial data by random sampling and the analysis of the experiment result.
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Hongxiao, F., Li, C., Jiabao, H., Yanru, X., Han, L. (2017). SANet: An Approach for Prediction in Music Trends. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_35
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DOI: https://doi.org/10.1007/978-981-10-3770-2_35
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