Algorithmic Orchestration: Deep Learning Techniques in Music Generation
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Abstract
Music generation using Deep Learning presents a comprehensive system for generating music in ABC notation using character based recurrent neural networks (RNNs), accompanied by a conversion pipeline that transforms the output music from ABC notation to a playable audio format. The integration of character-based RNNs allows for the creation of coherent and melodic musical compositions, while the ABC- to-MIDI-to WAV conversion enhances the usability and accessibility of the generated music. The work starts by preprocessing the ABC notation dataset and representing it at the character level. A character-based RNN, such as a long short-term memory (LSTM) network, is then employed to learn the sequential dependencies within the ABC notation and generate music that follows the learned patterns and structures. The RNN model is trained on a substantial corpus of ABC- encoded music, enabling it to capture the statistical regularities and nuances of the dataset. To make the generated music readily playable, the project incorporates a conversion pipeline that translates the output from ABC notation to MIDI format. MIDI files serve as a widely supported industry-standard representation of music, making them compatible with a variety of digital audio workstations (DAWs) and synthesizers. The MIDI files are subsequently converted to WAV format, a universally recognized audio format suitable for playback on diverse platforms and devices. This project contributes to the field of AI-generated music by providing an integrated system for music generation in ABC notation, accompanied by a seamless conversion pipeline for playback in the universally supported WAV audio format. The combination of character- based RNNs, ABC notation, and the ABC-to-MIDI-to-WAV conversion offers a valuable tool for musicians, composers, and music enthusiasts, facilitating the generation, sharing, and playback of musical compositions. Future research may explore advanced synthesis techniques, incorporate additional musical features, or investigate other music notation systems to further enhance the capabilities and usability of AI generated music.