Published Paper Details:

In the ever-evolving landscape of music consumption, the development of intelligent recommendation systems has become imperative to enhance user experience. This research paper introduces a pioneering approach to music recommendation by integrating advanced Convolutional Neural Networks (CNN) with face expression recognition. The proposed system aims to personalize music suggestions by analyzing users' facial expressions, extracting emotional cues, and aligning them with the corresponding auditory preferences. The convolutional neural network component of the system is designed to learn intricate patterns and features from music spectrograms, capturing both the audio content and underlying emotional nuances. Simultaneously, facial expression recognition technology is employed to discern users' emotional states during music listening sessions. By fusing these two modalities, our system strives to create a holistic understanding of users' preferences, considering both explicit musical features and implicit emotional responses. To achieve this integration, we leverage machine learning architectures for music analysis and facial expression recognition. A wide variety of facial expressions and musical genres are included in the dataset that the model is trained on. Additionally, the research explores the challenges and opportunities associated with combining these distinct modalities, such as data preprocessing, feature extraction, and model fusion. This research contributes to the ongoing discourse on the fusion of multimodal technologies for more nuanced and personalized recommendation systems, paving the way for innovative applications in the intersection of music and affective computing.