Deep Learning Methods for Sample-based Electronic Music

Sample-based electronic music (SBEM) encompasses various genres centred around the practice of sampling—the act of repurposing existing audio to create new music. Contemporary SBEM production involves navigating digital collections of audio, which include both libraries of samples and recorded music. The proliferation of digital music access, sample libraries, and online resource services have introduced challenges in navigating and managing these extensive collections of musical material. Selecting suitable samples from these sources is a meticulous and time-consuming task, requiring music producers to employ aesthetic judgement. Despite technological advancements, many SBEM producers still utilise laborious methods—established decades ago—for obtaining and manipulating music samples. This thesis proposes deep learning, a subfield of machine learning that develops algorithms to decipher intricate data relationships without explicit programming, as a potential solution. This research primarily explores the potential of deep learning models in SBEM, with a specific focus on developing automated tools for the analysis and generation of electronic music samples, towards enriching the creative experience for music producers. To this end, a novel deep learning system designed for automatic instrumentation role classification in SBEM is introduced. This system identifies samples based on their specific roles within a composition—such as melody, bass, and drums—and exhibits versatility across various SBEM production tasks. Through a series of experiments, the capacity of the system to automatically label unstructured sample collections, generate high-level summaries of SBEM arrangements, and retrieve samples with desired characteristics from existing recordings is demonstrated. Additionally, a neural audio synthesis system that facilitates the continuous exploration and interpolation of sounds generated from a collection of drum samples is presented. This system employs a generative adversarial network (GAN), further modified to interact with the generated outputs. The evaluations highlight the effectiveness of the proposed conditional style-based GAN in generating a diverse range of high-quality drum samples. Various systematic approaches for interacting with the network and navigating the generative space are investigated, demonstrating novel methods of sample manipulation. Collectively, these contributions aim to foster further exploration and advancements at the intersection of deep learning and SBEM