  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).
