이 글은 도심의 소리풍경soundscape 의미를 탐구하고 소리풍경의 체계적인 분류법을 발전시킬 방도를 모색한다. 연구자들이 보통 사용하는 일반적이 사항에서 시작해서 세부적인 것으로 접근하는 하향식 방법 대신, 이 문제는 상향식 접근의 소리경계적sono-sematic 소리풍경 분류학 발전전략에 기반한 “집단 청취collective listening”의 입장에서 많은 사람들의 의견을 수용(크라우드소싱)crowdsourcing하고 아우르는 방식을 택한다. 데이터정보 수집 시스템을 통해 전자음악의 상호적, 내부적 소리의미이론의 양식체계를 개발 및 구성할 상호작용누리꾼interactive online user에 대한 연구를 활성화한다.

Citygram is a multidisciplinary project that seeks to measure, stream, archive, analyze, and visualize spatiotemporal soundscapes. The infrastructure is built on a cyber-physical system that captures spatio-acoustic data via deployment of a flexible and scalable sensor network. This paper outlines recent project developments which includes updates on our sensor network comprised of crowd-sourced remote sensing, as well as inexpensive and high quality outdoor remote sensing solutions; development of a number of software tools for analysis, visualization, and development of machine learning; and an updated web-based exploration portal with real-time animation overlays for Google Maps. This paper also includes a summary of technologies and strategies that engage citizen scientist initiatives to measure New York City’s spatio-acoustic noise pollution in collaboration with the Center for Urban Science and Progress (CUSP).

Noise pollution is one of the most serious quality-of-life issues in urban environments. In New York City (NYC), for example, more than 80% of complaints1 registered with NYC’s 311 phone line2 are noise complaints. Noise is not just a nuisance to city dwellers as its negative implications go far beyond the issue of quality-of-life; it contributes to cardiovascular disease, cognitive impairment, sleep disturbance, and tinnitus3, while also interfering with learning activities [21]. One of the greatest issues in measuring noise lies in two of the core characteristics of acoustic noise itself — transiency and structural multidimensionality. Common noise measurement practices based on average noise levels are severely inadequate in capturing the essence of noise and sound characteristics in general. Noise changes throughout the day, throughout the week, throughout the month, throughout the year, and changes with respect to its frequency characteristics, energy levels, and the context in which it is heard. This paper outlines a collaborative project that addresses critical components for understanding spatiotemporal acoustics: measuring, streaming, archiving, analyzing, and visualizing urban soundscapes [28] with a focus on noise rendered through a cyber-physical sensor network system built on Citygram [23, 24].

In this paper we discuss our efforts in Soundscape Information Retrieval (SIR). Computational soundscape analysis is a key research component in the Citygram Project which is built on a cyber-physical system that includes a scalable robust sensor network, remote sensing devices (RSD), spatio-acoustic visualization formats, as well as software tools for composition and sonification. By combining our research in soundscape studies, which includes the capture, collection, analysis, visualization and musical applications of spatio-temporal sound, we discuss our current research efforts that aim to contribute towards the development of soundscape information retrieval (SIR). This includes discussion of soundscape descriptors, soundscape taxonomy, annotation, and data analytics. In particular, we discuss one of our focal research agendas in measuring and quantifying urban noise pollution.

This paper presents an exploration platform for locative sonification based on audio feature vectors extracted from urban spaces. Our locative sonification research is part of a larger project called Citygram[17]. Citygram focuses on geospatial research that is concerend with automatically collecting, visualizing, analyzing, and mapping nonocular energies from urban environments. The acoustic data is captured via off-the-shelf poly-sensory Androidbased remote sensing devices (RSD). Audio feature vectors are streamed to and stored in the Citygram database which can then be used for sonification and visualization. The first iteration, Citygram One, concentrates on urban acoustic energies, rendering spatio-acoustic feature vectors with the aim of better understanding our environment, large cities in particular. This paper focuses on using the Citygram framework for creative practice via locative sonification.