Abstract
The growing number of music content online has opened up new possibilities for the introduction of successful digital knowledge access services known as music referral systems that help user groups in searching, finding, sharing, and creating. The music recovery approach based on specific similarity information combines several similarity features, including audio and contextual similarities, such as tone format features and melodic details. Audio classification is very important for recovering audio files quickly. To get the best results from audio classification, it is important to choose the best feature set and follow the best analysis method. Support vector machines (SVMs) are implemented by learning from input samples to classify music into separate classes of music genres. The SVM study excelled in the music category classification.
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Narkhede, N., Mathur, S., Bhaskar, A. (2022). Automatic Classification of Music Genre Using SVM. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_33
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