Music serves as a universal medium for relaxation, emotional connection, and entertainment, offering solace after long workdays or enriching leisure moments. Beyond its role in dance and recreation, it resonates deeply with personal emotions, often mirroring the listener’s state of mind. But finding the perfect song for your mood or taste can be a tough task for some. This is where music fans want to be and know what category of music they’re interested in; however, getting them to the exact track they enjoy is tricky. This work is developing an intelligent genre classifier and providing personalized recommendation with the ease of use in music discovery. We take advantage of machine learning to automatically classify music into deep and fine grained categories, so that users can find their favorite music styles effortlessly. In Music Information Retrieval (MIR), automatic genre classification is an fundamental task. We concentrate on training and testing different machine learning models to achieve accurate and efficient music assemblage. This task is performed by three important algorithms: the K-Nearest Neighbours (KNN), the Support Vector Machines (SVM) and the Convolutional Neural Networks (CNN). The models are learned over the well-known GTZAN dataset that consists of 1,000 audio tracks with 1 min duration each, divided into 10 genres. Discriminative features, such as the waveform patterns and MFCCs, are extracted to represent each audio sample, which are then fed into the classifiers. This paper spans rigorous experimentation to measure the adequacy of machine learning methods in genre identification and overall pushes the state-of-the-art of next level music classification system.
Music genre classification, MIR systems, machine learning comparison, KNN algorithm, SVM classifier, CNN architecture, audio feature extraction, algorithmic performance
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