Classifying classical music’s therapeutic effects using deep learning: a review - Angelin, Angelin; Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Mental health issues are the leading cause of global disability, increasing the need for treatment options. While there is much research on the emotional recognition of music in general, there is a gap in studies that directly connect musical features with their therapeutic effects using deep learning. This systematic literature review explores the use of deep learning in classifying the therapeutic effects of classical music for mental health. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 15 papers were reviewed. This review synthesized studies on the role of musical elements that affect mental states. Different feature extraction methods, including mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features, are discussed for their roles in classifying these therapeutic effects. This review also looks at deep learning algorithms like convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) network, and combined models to assess their effectiveness. Common evaluation methods are also assessed to measure the performance of these models in audio classification. This review highlights the potential of combining deep learning with classical music for mental health support, and to future possibilities for applying these methods in the real world.