Deep Learning with Audio: An Explorative Syllabus for Music Composition and Production

This paper introduces a new course on deep learning with audio, designed specifically for graduate students in arts studies. The course introduces students the principles of deep learning models in audio and symbolic domain as well as their possible applications in music composition and production. The course covers topics such as data preparation and processing, neural network architectures, training and application of deep learning models in music related tasks. The course also incorporates hands-on exercises and projects, allowing students to apply the concepts learned in class to real-world audio data. In addition, the course introduces a novel approach to integrating audio generation using deep learning models in Pure Data realtime audio synthesis environment, which enables students to create original and expressive audio content in a programming environment that they are more familiar with. The variety of the audio content produced by the students demonstrates the effectiveness of the course in fostering creative approach to their own music productions. Overall, this new course on deep learning with audio represents a significant contribution to the field of artificial intelligence (AI) music and creativity, providing arts graduate students with the necessary skills and knowledge to tackle the challenges of the rapidly evolving AI music technologies.