Starte als Master thesis »Times Series Modeling of Noise Monitoring Data in Urban Environments« bei Digital Media Technology – Dein Job in Ilmenau

The Fraunhofer Institute for Digital Media Technology IDMT is part of the Fraunhofer-Gesellschaft. Headquartered in Ilmenau, Germany, the institute is internationally recognized for its expertise in applied electroacoustics and audio engineering, AI-based signal analysis and machine learning, and data privacy and security. At the headquarters, on the campus of “Technische Universität Ilmenau” researchers work on technologies for robust, trustworthy AI-based analysis and classification of audio and video data. These are used, among other things, to monitor industrial production processes, but also in traffic monitoring or in the media context, for example when it comes to automatic metadata extraction and audio manipulation detection. Another focus is the development of algorithms for the areas of virtual product development, intelligent actuator-sensor systems and audio for the automotive sector. There are currently around 70 employees working at Fraunhofer IDMT in Ilmenau.

What you will do

Excessive noise exposure in urban environments poses significant health risks to individuals. Prolonged exposure to high noise levels can lead to hearing loss, sleep disturbances, stress, and reduced cognitive performance. To study these dangers, acoustic monitoring technologies offer a valuable solution. These technologies enable real-time measurement and analysis of noise levels in urban areas, providing data that can inform noise control measures and urban planning strategies aimed at reducing noise exposure and improving overall well-being in urban environments.

In a joint R&D effort, Fraunhofer IDMT and IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH (IMMS GmbH) developed mobile sensor units, which are mounted on light poles and implement a real-time measurement of several loudness measures inspired by the TALärm [1], a current set of 27 different sound classes, as well as a recognition and classification of vehicles that pass the sensor. A previous version of the sensor, developed in the "Stadtlärm" research project, is documented in [2]. Currently, five sensors are deployed around the VELTINS arena in Gelsenkirchen, Germany [3]. The arena is a regular venue for sporting events and concerts, with many tens of thousands of spectators travelling
to and from the event.The overall goal of this thesis is to develop and evaluate different time series modeling and forecasting approaches based on traditional as well as on deep learning based techniques to study the sensor data recorded since pril 2023.

The following research questions shall be investigated:
(1) Which long-term repetition patterns can be observed (e.g. daily rush hour commuter traffic on nearby highways, arrival and departure of spectators at major events)
(2) What is the influence of the local weather conditions on the measurement data?
(3) Can a temporal correlation between high volume levels and the activity of certain sound types be determined?
(4) To what extent is it possible to predict the noise exposure around the ARENA during major events based on the measurements taken during previous (comparable) events?
(5) During major events, can certain safety-critical situations (panic, screams, gunshot sounds) and a subsequent deployment of the security forces (different siren types) be reconstructed on the basis of the measurement data?

In particular, in this Master's Thesis, the following objectives should be accomplished:

(1) Conduct a state-of-the-art researchon time series modeling approaches with special focus on their application for modeling and forecasting acoustic sensor data.

(2) Become familiar with the current sensor system, the implemented measures, as well as the access of the measurement data for analysis. Also, additional open databases, which can provide meteological data from the sensor locations shall be exploited.

(3) Re-implement 2-3 state-of-the-art time series modeling/forecasting methods and apply them to study the existing measurment data w.r.t. to the abovementioned research questions.

The student should document their work in a written thesis. The thesis will mainly be supervised at Fraunhofer IDMT in close cooperation with IMMS.

References:

[1] https://www.verwaltungsvorschriften-im-internet.de/bsvwvbund_26081998_IG19980826.htm

[2] J. Abeßer et al., "A Distributed Sensor Network for Monitoring Noise Level and Noise Sources in Urban Environments," 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), Ba