تقرير
Practical aspects for the creation of an audio dataset from field recordings with optimized labeling budget with AI-assisted strategy
| العنوان: | Practical aspects for the creation of an audio dataset from field recordings with optimized labeling budget with AI-assisted strategy |
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| المؤلفون: | Naranjo-Alcazar, Javier, Grau-Haro, Jordi, Ribes-Serrano, Ruben, Zuccarello, Pedro |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | Machine Listening focuses on developing technologies to extract relevant information from audio signals. A critical aspect of these projects is the acquisition and labeling of contextualized data, which is inherently complex and requires specific resources and strategies. Despite the availability of some audio datasets, many are unsuitable for commercial applications. The paper emphasizes the importance of Active Learning (AL) using expert labelers over crowdsourcing, which often lacks detailed insights into dataset structures. AL is an iterative process combining human labelers and AI models to optimize the labeling budget by intelligently selecting samples for human review. This approach addresses the challenge of handling large, constantly growing datasets that exceed available computational resources and memory. The paper presents a comprehensive data-centric framework for Machine Listening projects, detailing the configuration of recording nodes, database structure, and labeling budget optimization in resource-constrained scenarios. Applied to an industrial port in Valencia, Spain, the framework successfully labeled 6540 ten-second audio samples over five months with a small team, demonstrating its effectiveness and adaptability to various resource availability situations. Acknowledgments: The participation of Javier Naranjo-Alcazar, Jordi Grau-Haro and Pedro Zuccarello in this research was funded by the Valencian Institute for Business Competitiveness (IVACE) and the FEDER funds by means of project Soroll-IA2 (IMDEEA/2023/91). Comment: Submitted to ICML 2024 Workshop on Data-Centric Machine Learning Research |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.18153 |
| رقم الأكسشن: | edsarx.2405.18153 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |