Music Emotion Detection System Using AI/ML

The application of Artificial Intelligence (AI) and Machine Learning (ML) to detect emotions in music has gained significant attention in recent years. As music is widely considered to be a universal language, detecting the emotional state it conveys can have numerous applications in various industries, including entertainment, healthcare, and education. This paper presents a comprehensive review of the techniques used in Music Emotion Detection (MED) systems, emphasizing the role of AI and ML algorithms in enhancing the accuracy and efficiency of emotion classification. The research begins with an exploration of the theoretical framework of emotion in music and how it is influenced by various elements such as melody, rhythm, harmony, and lyrics. Following this, the paper delves into the different AI/ML models that have been employed in this domain, including supervised learning algorithms like Support Vector Machines (SVM), Random Forests, and deep learning approaches such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The paper also discusses the use of feature extraction techniques, such as Mel-frequency cepstral coefficients (MFCC), chroma features, and spectral features, to represent musical data effectively for emotion classification. Additionally, the challenges and limitations of existing systems are highlighted, including the complexity of accurately classifying emotions across diverse genres of music, and the subjectivity of emotional perception among listeners. The paper concludes with a discussion on the future of MED systems, focusing on the potential of integrating multi-modal approaches, where musical, textual, and contextual data can be combined for improved emotion detection. This work aims to provide an in-depth understanding of the current landscape of music emotion detection, offering insights for researchers and practitioners to advance the development of intelligent, emotion-aware music systems. DOI - https://doi.org/10.65525/SVUP.9788199651517.2026.107-112.