Automatic Environmental Sound Recognition: Performance versus Computational Cost

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council

Noise Mapping of an Educational Environment: A Case Study of South Dakota State University

Sound is the subjective dimension of what we hear when vibrations reach our ears. Noise, or unwanted sound (mostly human caused), is an objective function of the pressure of those vibrations and is often measured using decibels (dB). Noise is a type of pollution that has both direct and indirect negative impacts on humans, with significant implications for public health plus social, economic, and environmental well-being. Mapping the acoustic landscape (i.e., soundscape) using noise and sound data provides important insights for evaluating, interpreting, understanding, and managing environmental noise. The objectives of this research are threefold; to map the spatial and temporal patterns of the SDSU campus’ soundscape, to identify the dominant sound sources at various locations, especially “problem areas”, and to compare the quality of noise data collected from a smartphone application (SPA) and a traditional digital noise meter (DNM). A SPA and DNM were used to simultaneously collect noise level data at the same collection sites in the field. A digital audio recorder was also used to collect sound data, which were subsequently classified based on their source into one of four different categories: mechanical; natural; human; and, communications. Ordinary kriging was used to interpolate both noise and sound data. A t-test was used to compare the mean noise levels across different time periods and test for significant differences between noise data collected using the SPA and the DNM. Results clearly indicate that mechanical sound sources dominate SDSU’s soundscape. The noise levels captured by the DNM ranged between 43-67, 44-69, and 43-61 dBA during the morning, afternoon, and evening, respectively. Similarly, noise levels captured by the SPA ranged between 44-71, 38-65, and 41-64 dBA during the morning, afternoon, and evening, respectively. The t-test results indicate that mean noise levels measured from these two devices did not exhibit statistically significant differences. Mapping the noisescape and the soundscape allowed the identification of problem areas and it also provided important insights that can be used to mitigate environmental noise issues. The results could also be used to raise awareness of the social, economic, environmental, and public health implications of noise pollution

A hybrid intelligent agent for notification of users distracted by mobile phones in an urban environment

Mobile devices are now ubiquitous in daily life and the number of activities that can be performed using them is continually growing. This implies increased attention being placed on the device and diverted away from events taking place in the surrounding environment. The impact of using a smartphone on pedestrians in the vicinity of urban traffic has been investigated in a multimodal, fully immersive, virtual reality environment. Based on experimental data collected, an agent to improve the attention of users in such situations has been developed. The proposed agent uses explicit, contextual data from experimental conditions to feed a statistical learning model. The agent’s decision process is aimed at notifying users when they become unaware of critical events in their surroundings