Trainable data manipulation with unobserved instruments

Southall, Carl and Stables, Ryan and Hockman, Jason (2019) Trainable data manipulation with unobserved instruments. In: Workshop on Intelligent Music Production, 6th September 2019, Birmingham, UK.

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Machine learning algorithms are the core components in a wide range of intelligent music production systems. As training data for these tasks is relatively sparse, data augmentation is often used to generate additional training data by slightly altering existing training data. User-defined techniques require a long parameter tuning process and typically use a single set of global variables. To address this, a trainable data manipulation system, termed player vs transcriber, was proposed for the task of automatic drum transcription. This paper expands the player vs transcriber model by allowing unobserved instruments to also be manipulated within the data augmentation and sample addition stages. Results from two evaluations demonstrate that this improves performance and suggests that trainable data manipulation could benefit additional intelligent music production tasks.

Item Type: Conference or Workshop Item (Paper)
1 August 2019Accepted
6 September 2019Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-04 - software engineering
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH25 - design, and creative and performing arts > CAH25-02 - performing arts > CAH25-02-02 - music
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Jason Hockman
Date Deposited: 04 Apr 2022 15:24
Last Modified: 22 Mar 2023 12:01

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