AI-GENERATED MUSIC COMPOSITION USING PYTHON AND GANs

Abstract

In an era where technology increasingly influences our daily lives, "Emotional Echoes" harnesses the power of artificial intelligence to create a unique and personalized musical experience tailored to the user's emotional state. This project integrates advanced emotion recognition algorithms, real-time video processing, and AI-driven music generation to curate soundtracks that resonate with individual feelings. Utilizing Media Pipe for facial landmark detection and a Keras-based deep learning model for emotion classification, the system interprets users' emotions through their facial expressions and hand gestures. The application not only captures these emotional cues but also generates customized playlists or original compositions that reflect the user's mood, bridging the gap between emotional intelligence and musical creativity. By analysing user input alongside real-time emotion detection, "Emotional Echoes" recommends songs or generates music that enhances or complements the user's emotional journey. This innovative approach aims to deepen the connection between individuals and music, transforming how we experience sound in relation to our feelings. Ultimately, "Emotional Echoes" serves as a musical companion, fostering emotional well-being through tailored auditory experiences that inspire, uplift, and resonate with the human spirit. With advancements in artificial intelligence, the field of music composition has seen significant transformations, enabling machines to generate music that closely resembles human creativity. This project, explores the potential of deep learning techniques, particularly to autonomously create original musical compositions. The primary goal is to develop an AI system capable of learning musical structures, harmonies, and styles from a diverse dataset of existing compositions and using this knowledge to generate new, coherent, and aesthetically pleasing music.The project involves to optimize the quality of generated compositions. By training the GAN-based model on a rich dataset of musical pieces, the system learns intricate patterns, including melody, rhythm, and chord progressions, allowing it to generate compositions across multiple genres. The project also to refine musical creativity and structure. Furthermore, this research explores the challenges associated with AI-generated music, such as maintaining musical coherence, preventing redundancy, and ensuring emotional depth in compositions.