Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications

Abstract

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.

Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network

“…[48]. Recently, the same authors of this current work presented a short measurement campaign in which the potential of the application of WASN at university buildings was shown [23].…”

“…A wireless acoustic sensor network is composed of a set of nodes [29] which are deployed in the area of interest to continuously monitor, collect and analyze acoustic parameters values for long periods of time. WASNs can be classified in two categories depending on their nodes spatial location: fixed [23] or mobile [30] stations, that includes the mobile participatory measurement networks like smartphones. Wireless communication technologies add to the devices an ease of deployment and the ability to capture data in near real-time, store and obtain big data statistics [31] and also create dynamic noise maps [32].…”

“…According to Tristán Hernández et al [21], the main source of noise is the one generated by the students themselves in their daily activities and it is usually amplified in areas with non-existent acoustic treatment such as common rooms and corridors. Different investigations have been developed in university facilities such as classroom room-acoustics and insulation [22], noise annoyance in staff offices and departments [23], together with a comparative study related with noise at two university campus [24]. However, in most of these previous works, noise annoyance is a parameter commonly evaluated with objective sound indicators, such as the equivalent sound pressure level, measuring sound energy which is a physical descriptor.…”

Analysis on the Acoustics of an Auditorium

“…Acoustic research is used in many fields, and architecture and urban construction are very important parts [7] [8] [9]. Initially, the design of the church considered the influence of sound reflection, reverberation time and other factors on the audience [10] [11] [12] [13] [14].…”

Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks

“…Recently there has been a very significant increase in the number of devices available for monitoring and analysing environmental sounds. This increase has occurred in urban areas [ 1 , 2 , 3 ] and in environmental control operations, for example using the acoustic emission spectrum of forest fires to classify the type of forest fire [ 4 ]. The problem of analysis and classification of the sounds emitted by some species of the animal kingdom is one of the main applications of the monitoring of environmental sounds.…”