Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great potential for compact systems that use MIR algorithms, such as digital musical instruments. However, embedded computing hardware is generally resource constrained, posing additional limitations. In this paper, we identify and discuss the challenges and limitations of embedded real-time MIR. Furthermore, we discuss potential solutions to these challenges, and demonstrate their validity by presenting an embedded real-time classifier of expressive acoustic guitar techniques. The classifier achieved 99.2% accuracy in distinguishing pitched and percussive techniques and a 99.1% average accuracy in distinguishing four distinct percussive techniques with a fifth class for pitched sounds. The full classification task is a considerably more complex learning problem, with our preliminary results reaching only 56.5% accuracy. The results were produced with an average latency of 30.7 ms.

ON THE CHALLENGES OF EMBEDDED REAL-TIME MUSIC INFORMATION RETRIEVAL / Stefani, D.; Turchet, L.. - 3:(2022), pp. 177-184. (Intervento presentato al convegno 25th International Conference on Digital Audio Effects, DAFx 2022 tenutosi a Vienna nel 2022).

ON THE CHALLENGES OF EMBEDDED REAL-TIME MUSIC INFORMATION RETRIEVAL

Stefani D.
Primo
;
Turchet L.
Ultimo
2022-01-01

Abstract

Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great potential for compact systems that use MIR algorithms, such as digital musical instruments. However, embedded computing hardware is generally resource constrained, posing additional limitations. In this paper, we identify and discuss the challenges and limitations of embedded real-time MIR. Furthermore, we discuss potential solutions to these challenges, and demonstrate their validity by presenting an embedded real-time classifier of expressive acoustic guitar techniques. The classifier achieved 99.2% accuracy in distinguishing pitched and percussive techniques and a 99.1% average accuracy in distinguishing four distinct percussive techniques with a fifth class for pitched sounds. The full classification task is a considerably more complex learning problem, with our preliminary results reaching only 56.5% accuracy. The results were produced with an average latency of 30.7 ms.
2022
Proceedings of the International Conference on Digital Audio Effects, DAFx
Basel, Switzerland
MDPI (Multidisciplinary Digital Publishing Institute)
978-3-200-08599-2
Stefani, D.; Turchet, L.
ON THE CHALLENGES OF EMBEDDED REAL-TIME MUSIC INFORMATION RETRIEVAL / Stefani, D.; Turchet, L.. - 3:(2022), pp. 177-184. (Intervento presentato al convegno 25th International Conference on Digital Audio Effects, DAFx 2022 tenutosi a Vienna nel 2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/364735
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