Improved automatic instrumentation role classification and loop activation transcription

Drysdale, Jake, Hockman, Jason, Ramires, António and Serra, Xavier (2022) Improved automatic instrumentation role classification and loop activation transcription. In: 25th International Conference on Digital Audio Effects (DAFx20in22), 06 September 2022 - 10 September 2022, Vienna, Austria.

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Abstract

Many electronic music (EM) genres are composed through the activation of short audio recordings of instruments designed for seamless repetition—or loops. In this work, loops of key structural groups such as bass, percussive or melodic elements are labelled by the role they occupy in a piece of music through the task of automatic instrumentation role classification (AIRC). Such labels assist EM producers in the identification of compatible loops in large unstructured audio databases. While human annotation is often laborious, automatic classification allows for fast and scalable generation of these labels. We experiment with several deep learning architectures and propose a data augmentation method for improving multi-label representation to balance classes within the Free sound Loop Dataset. To improve the classification accuracy of the architectures, we also evaluate different pooling operations. Results indicate that in combination with the data augmentation and pooling strategies, the proposed system achieves state-of-the art performance for AIRC. Additionally, we demonstrate how our proposed AIRC method is useful for analysing the structure of EM compositions through loop activation transcription.