CNN with attention

This project developed a convolutional neural network (CNN) designed to analyze hand postures in trumpet playing. The model integrated self-attention mechanisms to enhance feature extraction and focused on distinguishing subtle hand movements critical for proper technique. Data augmentation was extensively used to address variability in lighting, angles, and hand positions. The project aimed to assist music educators and students by providing automated feedback on technique, paving the way for intelligent tools in music education. Performance was evaluated on a custom dataset with promising results in accuracy and generalization.