Towards Computer-Assisted Flamenco Transcription: An Experimental Comparison of Automatic Transcription Algorithms as Applied to A Cappella Singing

Author:

Gómez Emilia1,Bonada Jordi1

Affiliation:

1. Music Technology Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra Roc Boronat, 138 08018 Barcelona, Spain.

Abstract

This article deals with automatic transcription of flamenco music recordings—more specifically, a cappella singing. We first study the specifics of flamenco singing and propose a transcription system based on fundamental frequency and energy estimation, which incorporates an iterative strategy for note segmentation and labeling. The proposed approach is evaluated on a music collection of 72 performances, including a variety of singers and recording conditions, and the presence or absence of percussion, background voices, and noise. We obtain satisfying results for the different approaches tested, and our system outperforms a state-of-the-art approach designed for other singing styles. In this study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main limitations of our approach and discuss challenges for future studies.

Publisher

MIT Press - Journals

Subject

Computer Science Applications,Music,Media Technology

Cited by 29 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis of Supraglottic Activity during Vocalization in Flamenco Singers;Folia Phoniatrica et Logopaedica;2024-07-19

2. Automatic Lyric Transcription and Automatic Music Transcription from Multimodal Singing;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-05-16

3. Note-level singing melody transcription with transformers;Intelligent Data Analysis;2023-11-20

4. A Comprehensive Review on Music Transcription;Applied Sciences;2023-10-30

5. Training a Singing Transcription Model Using Connectionist Temporal Classification Loss and Cross-Entropy Loss;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023

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