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.

Underwater noise pollution due to marine traffic can have adverse effects on marine animals. This is the first study of modeling underwater noise levels from shipping in the Slovenian Sea. The aim of the study was to select and develop an appropriate methodology for modeling underwater noise levels. The logarithmic model was used to describe the range-dependent attenuation of low-frequency sound. The parameters of the model were determined from the transmission loss function calculated along ten selected cross sections across the North Adriatic using the parabolic equation. The difference between the measured and modeled levels was within ±5 dB. However, the model was able to identify trends in underwater noise levels that could not be identified by the measurements, suggesting that noise maps could provide a basis for management purposes. In building the model, we found that the modeled values are mainly affected by the speed of the ships. Therefore, adjusting the speed of ships could be an effective measure to be taken into account for controlling underwater noise from shipping in the world’s seas and oceans.

This study investigates the sound absorption properties of shredded recycled plastic particles, with a focus on how particle size influences low-frequency acoustic performance. Given the growing environmental challenge posed by plastic waste, this research aims to repurpose such materials into effective, sustainable sound absorbers, contributing to both noise reduction and waste minimization efforts. The sound absorption coefficients and normalized surface impedances of different size fractions, ranging from 45 µm to 3 mm, were measured using the standard two-microphone impedance tube method in accordance with ISO 10534-2. The analysis revealed that smaller particle sizes significantly enhance low-frequency absorption, though at the expense of the maximum absorption coefficient across a broader frequency range. This trade-off underscores the importance of particle size in designing materials tailored to specific acoustic applications. Results indicate that optimal sound absorption shifts towards higher frequencies for larger particle sizes, while finer particles are more effective at lower frequencies. The ability to customize the acoustic properties of shredded plastic particles by selecting appropriate particle sizes is a key outcome of this study. Microscopic examination of the particles revealed a correlation between particle morphology and acoustic performance, with irregular shapes contributing to enhanced sound energy dissipation. The findings suggest that shredded recycled plastic particles are a viable and sustainable option for creating customizable sound-absorbing materials, particularly in applications where low-frequency noise reduction is critical. This research not only advances the understanding of acoustic performance in recycled materials but also supports broader sustainability goals by offering an innovative use for plastic waste in industrial and architectural noise control solutions. In conclusion, this study provides a foundation for the development of eco-friendly, effective sound-absorbing materials from recycled plastics, emphasizing the importance of particle size in achieving desired acoustic properties.

Measuring the sound absorption coefficient in a small reverberation room below the Schroeder frequency is a challenging task and the results are subject to a high degree of uncertainty. Recent research shows that the low-frequency acoustic properties of the enclosure and the test sample should be characterized by quantities that describe the modal sound field. The aim of this paper is to present a method for estimating the absorption coefficient of a test sample at modal frequencies in the low frequency range and to demonstrate its applicability in a small room. The hybrid inverse approach is based on eigenvalue measurement data coupled with a finite element model to estimate the sound-absorbing properties of a small room and the test sample. The method was tested experimentally and the results were consistent with those of the long impedance tube method. The applicability of the proposed method was further demonstrated by the in-situ characterization of different acoustic samples in the low frequency range.