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.

机器听觉的研究聚焦于开发从音频信号中提取相关信息的技术，本文强调了在获取和标注具有上下文的数据方面的重要性，提出了一种综合的数据中心框架用于机器听觉项目，在资源受限的情况下实现配置录音节点、数据库结构和标注预算优化，通过使用专家标注员而非众包实现主动学习，以解决处理大规模、不断增长的数据集带来的计算资源和内存限制等问题。

利用人工智能辅助策略优化标注预算的野外录音数据集创建实务方面的问题

Practical aspects for the creation of an audio dataset from field  recordings with optimized labeling budget with AI-assisted strategy

We present the Sounds of New York City (SONYC) project, a smart cities
initiative focused on developing a cyber-physical system for the monitoring,
analysis and mitigation of urban noise pollution. Noise pollution is one of the
topmost quality of life issues for urban residents in the U.S. with proven
effects on health, education, the economy, and the environment. Yet, most
cities lack the resources to continuously monitor noise and understand the
contribution of individual sources, the tools to analyze patterns of noise
pollution at city-scale, and the means to empower city agencies to take
effective, data-driven action for noise mitigation. The SONYC project advances
novel technological and socio-technical solutions that help address these
needs.
SONYC includes a distributed network of both sensors and people for
large-scale noise monitoring. The sensors use low-cost, low-power technology,
and cutting-edge machine listening techniques, to produce calibrated acoustic
measurements and recognize individual sound sources in real time. Citizen
science methods are used to help urban residents connect to city agencies and
each other, understand their noise footprint, and facilitate reporting and
self-regulation. Crucially, SONYC utilizes big data solutions to analyze,
retrieve and visualize information from sensors and citizens, creating a
comprehensive acoustic model of the city that can be used to identify
significant patterns of noise pollution. These data can be used to drive the
strategic application of noise code enforcement by city agencies to optimize
the reduction of noise pollution. The entire system, integrating cyber,
physical and social infrastructure, forms a closed loop of continuous sensing,
analysis and actuation on the environment.
SONYC provides a blueprint for the mitigation of noise pollution that can
potentially be applied to other cities in the US and abroad.

Sounds of New York City (SONYC) 项目是面向城市噪声污染监测、分析和减轻的智能城市项目，它采用低成本、低功耗的技术和先进的机器听取技术来制定城市的全面声学模型，利用大数据解决方案来分析、检索和可视化来自传感器和公民的信息，并在城市局等机构的支持下优化噪声污染的减缓。