INTRODUCTION

In 2012, we made a groundbreaking achievement by introducing the use of microphone arrays into long-term noise monitoring to study the effects of environmental and industrial noise on humans. Our work was the result of the ARRS Research Program P2-0354: Noise Control and its Effects on People. We reconfigured microphone arrays to identify the dominant noise sources in real-time, by detecting the direction of sound sources in polar coordinates on horizontal plane. In the SANSIC project (System for Automatic Noise Source Identification and Classification), we developed algorithms that required only four microphones. Methodology was patented in 2015. In 2018 we introduced a new parameter for describing complex acoustic environments, called the Immission Directivity. This parameter enabled spatial filtering of sound events, allowing for the extraction of spatial features of sound events. We then combined the spatial features extracted from the Immission Directivity with psychoacoustic features of sound events, for implementation in Artificial Intelligence (AI) algorithms (K-means, Self-Organizing Maps, and ANN).

Project proposes a methodology based on a network of IoT microphone arrays, which will allow localization of each sound event in every 50 milliseconds, and its classification. The methodology will enable tracking and counting of selected species producing sounds within a designated space. It will also allow for counting the number of different animals producing sound within the observed area. The proposed methodology is therefore a game-changer for monitoring biodiversity and for noise mapping in urban environments. Concept of “Advanced Sound Maps” is shown in Fig 1.

Fig. 1. Concept of Advanced Sound Maps based on the network of microphone arrays, left) in the natural environment to assess biodiversity and right) proof of concept in an urban/industrial environment for the purpose of noise abatement planning. 

Excessive noise pollution from traffic is the second most important environmental cause of health problems in Europe, with an annual cost of over 2.7 billion euros. The EU has adopted the EU Action Plan: “Towards a Zero Pollution for Air, Water and Soil” as part of the European Green Deal to reduce the proportion of people chronically disturbed by traffic noise by 30%. Simultaneously, the decline in biodiversity has become a critical issue as species and populations disappear from ecosystems at the rate of a mass extinction event. Biodiversity provides numerous ecosystem services that are essential for human well-being and survival, making the conservation of biodiversity critical. The EU has developed the 2030 Biodiversity Strategy to protect and restore habitats, conserve species and ecosystems, and ensure that biodiversity is integrated into all sectors and activities.

The proposed methodology for noise monitoring addresses both issues. Advanced Sound Maps (ASM) with identified noise sources can be synthesized from data obtained from a network of IoT microphone arrays measuring Immission Directivity, enabling efficient control of noise pollution in urban areas. By combining the Immission Directivity” with psychoacoustic features of sound events, AI algorithms can be used to classify different sound sources, providing valuable information to policy makers to identify and address specific noise sources and their impact on human health and well-being. The proposed methodology has the potential to make a significant contribution to addressing urban noise pollution, a growing problem in many cities worldwide.

In addition, effective biodiversity monitoring tools are critical to the success of biodiversity conservation. Automated sensors for remote monitoring, combined with machine learning classification methods, are effective in identifying species in images or recordings. Acoustic monitoring is a promising approach to biodiversity monitoring, given that many animals use sound signals for communication. This method is cost-effective, requires fewer personnel, and can be conducted without human presence, making it a valuable tool for monitoring animal presence over time.

Despite the significant impact of noise on the environment, biodiversity, and quality of life, no research projects on this topic have been funded in Slovenia in the last 8 years. The proposed project has the potential to make a significant contribution to addressing noise pollution and biodiversity and is consistent with EU environmental objectives. We believe that this project is essential for the progress of environmental research in Slovenia and should be approved for funding.

OBJECTIVES OF THE PROPOSED RESEARCH PROJECT

This interdisciplinary research project aims to build on prior research achievements in the field of noise measurement methods, noise mapping, identification and classification of noise events, and the reduction of their impact on humans. By achieving three objectives, the project supports the EU’s goal of achieving zero air pollution as outlined in relevant documents.

Objective 1: Develop and implement a new method for real-time localization and classification of sound events using a network of IoT microphone arrays and Immission Directivity. This will enable the creation of new types of sound event maps for both urban and natural environments, supporting the EU and Slovenian initiatives for creating DIGITAL TWINS.

Objective 2: Develop and test a new method for monitoring biodiversity in natural environments using a network of IoT microphone arrays, which will be capable of precise localization of individuals in the field. 

Objective 3: Provide a new methodology for data mining from the network of IoT microphone arrays, which will lead to more efficient and cost-effective noise abatement. This will help to reduce noise annoyance, sleep disorders, and health risks, supporting the EU’s Zero pollution action plan.

 The project’s objectives will be accomplished through research efforts that involve proving four hypotheses.

Hypothesis 1: “An advanced sound map” indicating the location of the sound source and its class/type can be synthesized when features of the sound event detected by the IoT microphone array and the parameter Immission directivity, are extracted simultaneously in real time and recorded. The location can be calculated using advanced triangulation methods or inverse methods. Classification can be done by a combination of supervised classification for spatial sound event features and unsupervised, self-growing classification for sound signal features.

Hypothesis 2: Features extracted from sound event signals, including psychoacoustic features and features extracted from spatial filtering, can provide an effective two-stage classification of sound events that combines supervised and unsupervised classification methods.

Hypothesis 3: “Advanced Noise Map” can be used to plan and optimize noise mitigation strategies, resulting in a significant reduction of noise annoyance, sleep disturbance, and health risks for humans.

Hypothesis 4: Algorithms for unsupervised classification, in combination with algorithms for optimization of the number of clusters/classes (such as the elbow method, the silhouette optimization, the gap statistic, and the Davies-Bouldin index), can be used to accurately assess the number and diversity of biological sound events within the network of microphone arrays placed in natural environments, and to provide valuable information for the assessment of biodiversity.