From sonic experiences to urban planning innovations
Bibliographic Data
| ID | 21601634 |
|---|---|
| Authors | Aura Kaarivuo (0000-0002-4514-3879, School of Media, Design and Conservation, Metropolia University of Applied Sciences, Helsinki, Finland, corresponding author), Kari Salo (0000-0002-6158-1131, School of ICT Metropolia, University of Applied Sciences, Espoo, Finland), Tommi Mikkonen (0000-0002-8540-9918, University of Helsinki) |
| Year | 2024 |
| Volume | 32 |
| Issue | 2 |
| Pages | 302-319 |
| Publication date | 2024-02-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | European Planning Studies (JOURNAL) |
| Journal identifiers | ISSN: 0965-4313 • E-ISSN: 1469-5944 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09654313.2021.1988062 |
| OpenAlex | W3205710685 |
| Language | EN |
| Citations received | 2 |
| References cited | 14 |
It is widely accepted that personal responses to soundscapes are more dependent on listeners’ emotions and attitudes, than on sounds or their physical features alone. Fast-growing cities have catalyzed the importance of designing urban spaces that citizens find pleasant and homely and that support a communal style of living. Unfortunately, there are no standardized methods or techniques to translate sonic experiences into measurable and reliable data, which urban planning professionals or the building industry could turn into innovations and solutions. Most of the data pertaining to noise pollution and city soundscapes is still based on predictive acoustic models and rarely takes any real-life experiences or physical measurements into consideration. This paper presents the concept of a smart and participatory approach for gathering sonic experiences that could be translated into measurable values. The aim is to search for data collection methods to provide data to train deep learning. With machine learning methods, it is possible to find patterns in both desirable and undesirable urban soundscapes. The aim of this concept is to create crowdsourced data collection methods and improve the understanding and communication between citizens and planning processes by producing more accurate and comparable experiential data
Acoustics · Big data · Citizen journalism · Civil engineering · Data collection · Data mining · Data science · Experiential learning · Human–computer interaction · Mathematics education · Sociology · Soundscape · Urban planning · World Wide Web · Computer Science · Engineering · Music and Audio Processing · Noise Effects and Management · Psychology · Speech and Audio Processing
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |