What is it about?
This work provides methods for the acoustic classification of guitar tunings with deep learning. Guitar tuning classification is the identification of a particular guitar tuning from a recording that features a guitar performance. A wide variety of guitar tunings feature in many different genres. Guitar tuning classification is useful for tasks such as music transcription and audio tagging.
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Why is it important?
This work (i) proposes a system that, when given an audio clip, returns a decision regarding the guitar tuning present in the recording, (ii) maps out a problem space for the nascent topic of guitar tuning classification, (iii) provides a dataset for guitar tuning classification tasks. Additionally, we provide evidence that deep learning can be used for the acoustic classification of guitar tunings.
Perspectives
I hope this work will motivate further research in this area as the development of a robust system for the acoustic classification of guitar tunings would provide the following benefits: (i) guitar tuning tags could be assigned to music recordings; these tags could be used to better organise, retrieve, and analyse music in digital libraries, (ii) tuning classification could be integrated into an automatic music transcription system, thus facilitating the production of more accurate and fine-grained symbolic representations of guitar recordings, (iii) insights acquired through guitar tunings research, would be helpful when designing systems for indexing, analysing, and transcribing other string instruments.
Edward Hulme
Cardiff University
Read the Original
This page is a summary of: Acoustic Classification of Guitar Tunings with Deep Learning, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3660570.3660574.
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