A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning

TL;DR

This paper introduces a data-centric framework utilizing active learning for efficient large-scale audio data acquisition and labeling in machine listening, demonstrating successful application in an industrial port setting.

Contribution

It presents a comprehensive framework for resource-efficient data collection and labeling in machine listening, emphasizing active learning over crowdsourcing and detailing system configuration and optimization strategies.

Findings

Labeled 6540 audio samples over five months with a small team.

Demonstrated framework's effectiveness and adaptability in resource-constrained environments.

Optimized labeling budget through active learning in large-scale datasets.

Abstract

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). The research carried out for this publication has been partially funded by the project STARRING-NEURO (PID2022-137048OA-C44) funded by the Ministry of Science, Innovation and Universities of Spain and the European Union.