Urban Rhapsody: Large‐scale exploration of urban soundscapes
Urban Rhapsody: Large‐scale exploration of urban soundscapes
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DOI：
10.1111/cgf.14534
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发表时间：
2022-05
影响因子：
2.5
通讯作者：
J. Rulff;Fábio Miranda;Maryam Hosseini;Marcos Lage;M. Cartwright;Graham Dove;J. Bello;Cláudio T. Silva
中科院分区：
文献类型：
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作者：
J. Rulff;Fábio Miranda;Maryam Hosseini;Marcos Lage;M. Cartwright;Graham Dove;J. Bello;Cláudio T. Silva
噪音是城市环境中主要的生活质量问题之一。除了烦恼之外，噪音还对公共健康和教育表现产生负面影响。虽然可以部署低成本传感器以高时间分辨率监测环境噪声水平，但它们产生的数据量和这些数据的复杂性构成了重大的分析挑战。解决这些挑战的一种方法是通过机器收听技术，该技术用于提取特征，试图对噪声源进行分类，并了解城市噪声状况的时间模式。然而，城市环境中的噪声源数量巨大，加上标记数据的稀缺性，几乎不可能创建具有足够大词汇量的分类模型，以捕捉城市声景的真正动态。在本文中，我们首先确定了一套要求，在尚未开发的城市声景探索领域。为了满足需求并应对已确定的挑战，我们提出了Urban Rhapsody，这是一个结合了最先进的音频表示，机器学习和视觉分析的框架，允许用户交互式地创建分类模型，了解城市的噪声模式，并快速检索和标记音频摘录，以创建大型高精度注释的城市录音数据库。我们展示了该工具的效用，通过案例研究领域专家使用的数据在纽约市的一个一类传感器网络的五年部署。
Noise is one of the primary quality‐of‐life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low‐cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state‐of‐the‐art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high‐precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five‐year deployment of a one‐of‐a‐kind sensor network in New York City.