Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

An adaptive muffler with a flexible internal structure is considered. Flexibility is achieved using controlled flow valves. The proposed adaptive muffler is able to adapt to changes in engine operating conditions. It consists of a Helmholtz resonator, expansion chamber, and quarter wavelength resonator. Different combinations of the control valves' states at different operating conditions define the main working principle. To control the valve's position, an active noise control approach was used. With the proposed muffler, the transmission loss can be increased by more than 10 dB in the selected frequency range.

The Zero Crossing Signature (ZCS) approach is a novel time-domain feature extraction method that analyzes zero crossing points over multiple amplitude-shifted versions of an acoustic signal, enabling richer information extraction while maintaining computational efficiency. This method is especially suitable for real-time classification in the emerging Internet of Things (IOT) landscape, where resource-constrained devices require low-power solutions to support emission reduction efforts. In this study, the ZCS method was employed to showcase its full potential by classifying vehicles as diesel or gasoline based on their acoustic signatures. This classification task, applied to a database of car sounds acquired in the authors’ previous research, serves as a comprehensive demonstration of the method’s capabilities in distinguishing between engine types through characteristic sound wave patterns, highlighting its effectiveness and applicability in real-world scenarios. To further enhance feature extraction while keeping computational costs low, simple transformations using the first and second derivatives of the acoustic signals were applied, offering an efficient means of capturing additional signal characteristics. A dataset of 417 vehicle recordings was analyzed, and the classification performance of ZCS was compared with the conventional Zero Crossing (ZC) method using a Self-Organizing Map (SOM) configured with a 1D grid of 9 neurons. The study evaluated various time constants and crossing threshold densities for ZCS, benchmarking them against the classical ZC approach to assess their effectiveness.