Abstract
Musical genres are categorical classifications that are used to distinguish between different types of music. Each genre differs from other genres in certain musical features. In pre-computational intelligence era, music genre categorization has traditionally been performed manually, mostly due to the lack of modern human—computer interaction concept, and obviously for the lack of enough computational processing abilities of the computers. However, with the ever-increasing number of digital music and vast features, genre recognition using Neural Network is producing a wide range of results across a variety of experiments recently. By studying and extracting information on such features, with applying relevant Neural Network algorithm and technique, and also exploring some new recognition techniques on the same dataset which has been used in established research works, we hope to discover and gather new information about genre classification, and further understand future potential directions and prospects that could improve the art of computational musical genre recognition, decomposition of the clustered data corpus, and as a whole construction thereafter.
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Das, S., Kolya, A.K. (2019). A Theoretic Approach to Music Genre Recognition from Musical Features Using Single-Layer Feedforward Neural Network. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_14
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DOI: https://doi.org/10.1007/978-981-13-1951-8_14
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