User-Driven Fine-Tuning for Beat Tracking

• Type: Article in International Scientific Journal
• Date: 2021

Authors

António S. Pinto (Author) Other; Sebastian Böck (Author) Other; Jaime S. Cardoso (Author) FEUP; Matthew E. P. Davies (Author) Other

Journal

Title: Electronics (Switzerland)

Vol. 10

Pages: 1-1518

ISSN: 2079-9292

Publisher: MDPI

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00V-373

DOI: 10.3390/electronics10131518

Abstract (EN): The extraction of the beat from musical audio signals represents a foundational task in the field of music information retrieval. While great advances in performance have been achieved due the use of deep neural networks, significant shortcomings still remain. In particular, performance is generally much lower on musical content that differs from that which is contained in existing annotated datasets used for neural network training, as well as in the presence of challenging musical conditions such as rubato. In this paper, we positioned our approach to beat tracking from a real-world perspective where an end-user targets very high accuracy on specific music pieces and for which the current state of the art is not effective. To this end, we explored the use of targeted fine-tuning of a state-of-the-art deep neural network based on a very limited temporal region of annotated beat locations. We demonstrated the success of our approach via improved performance across existing annotated datasets and a new annotation-correction approach for evaluation. Furthermore, we highlighted the ability of content-specific fine-tuning to learn both what is and what is not the beat in challenging musical conditions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23