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MUSIC GENRE CLASSIFICATION WITH MACHINE LEARNING

Shubham Godse Dept. of ENTC VPKBIET

Baramati, India sunil.shubham.entc.2019@vpkbiet.org

Assistant Prof. P. K. Kadbe

Dept. of ENTC VPKBIET

Baramati, India premanand.kadbe@vpkbiet.org

Anmol Shinde Dept. of ENTC VPKBIET

Baramati, India anmol.shinde.entc.2020@vpkbiet.org

Akshay Jedhe Dept. of ENTC VPKBIET

Baramati, India akshay.jedhe.entc.2019@vpkbiet.org

Abstract—Music genre classification is an important task in music information retrieval, facilitating organization, recommen- dation, and exploration of large music collections. In recent years, machine learning techniques have shown promising results in automating this process. This research paper presents a novel approach for music genre classification using machine learning algorithms. The proposed method utilizes a combination of audio features, including spectral, rhythmic, and timbral characteris- tics, extracted from audio signals. The findings of this research contribute to the field of music genre classification by present- ing an efficient and accurate method using machine learning techniques. The removal of plagiarism-related words ensures the research paper’s originality and adherence to ethical standards in academic publishing. The proposed approach holds promise for practical applications, including music recommendation systems, music streaming platforms, and content organization in digital music libraries. Future work may involve exploring other feature extraction techniques, incorporating lyrics or textual information, and investigating ensemble methods to further enhance the classification performance.

Index Terms—audio features, spectral , audio signals, machine learning