MACHINE LEARNING-BASED ASSESSMENT AND PREDICTION OF PSYCHOACOUSTIC INDICATORS OF ROAD TRAFFIC NOISE

Year 2025, Volume: 13 Issue: 3, 675 - 686, 30.09.2025

Şebnem Karahançer

https://doi.org/10.21923/jesd.1675666

Abstract

This study explores the use of machine learning techniques to evaluate and predict psychoacoustic noise characteristics associated with pass-by road traffic events. Using a dataset comprising various acoustic and psychoacoustic parameters such as LAeq, FS50, L10, L90, and spectral indicators, a comprehensive analysis was conducted to assess their predictive potential. Gradient Boosting, Random Forest, and ARIMA models were employed for different tasks, including both classification and time-series forecasting. In addition, feature engineering techniques were used to create composite variables and enhance model input quality, while sequence-based learning methods allowed for temporal dynamics to be captured. The best-performing Gradient Boosting model achieved R² = 0.63 and MAE = 0.122 in predicting LAeq and FS50 indicators. The dataset used consisted of 1,200 pass-by noise events from an open-access repository, including both acoustic (LAeq, L10, L90) and psychoacoustic (FS50, R50, N50, S50) metrics. The results highlight the capability of machine learning not only to improve the accuracy of psychoacoustic modeling but also to support real-time, perception-aware urban noise monitoring systems. Such approaches can enable more responsive and adaptive noise management strategies in smart city planning. These findings demonstrate the potential of ML-based models to inform proactive urban noise management and public health strategies.

Keywords

Psychoacoustics , Pass-by noise , Machine learning , Acoustic indicators.

YOL TRAFİK GÜRÜLTÜSÜNÜN PSİKOAKUSTİK GÖSTERGELERLE MAKİNE ÖĞRENMESİ TABANLI DEĞERLENDİRİLMESİ VE TAHMİNİ

Abstract

Bu çalışma, yoldan geçen trafik olaylarına ait psikoakustik gürültü özelliklerini değerlendirmek ve tahmin etmek amacıyla makine öğrenimi tekniklerinin kullanımını araştırmaktadır. LAeq, FS50, L10, L90 ve spektral göstergeler gibi çeşitli akustik ve psikoakustik parametreleri içeren bir veri seti kullanılarak, bu göstergelerin tahmin gücü kapsamlı bir şekilde analiz edilmiştir. Çalışmada, sınıflandırma ve zaman serisi tahmini gibi farklı görevler için Gradient Boosting, Random Forest ve ARIMA modelleri uygulanmıştır. Ayrıca, özellik mühendisliği teknikleriyle bileşik değişkenler oluşturulmuş, model girişlerinin niteliği artırılmıştır. Zamansal örüntüleri yakalayabilen sequence-based yöntemler ile geçiş verileri üzerinde daha gerçekçi analizler yapılmıştır. Modellerin, farklı trafik ve çevre koşullarında gürültü rahatsızlığı düzeylerini tahmin etme yetenekleri de değerlendirilmiştir. Bulgular, makine öğreniminin yalnızca psikoakustik modelleme doğruluğunu artırmakla kalmayıp, aynı zamanda gerçek zamanlı, algıya duyarlı kentsel gürültü izleme sistemlerini desteklemede de güçlü bir araç olduğunu göstermektedir. Bu tür yaklaşımlar, akıllı şehir planlaması kapsamında daha esnek ve uyarlanabilir gürültü yönetim stratejilerinin geliştirilmesini mümkün kılmaktadır.

Keywords

Psikoakustik , Geçiş Gürültüsü , Makine Öğrenimi , Akustik Göstergeler

References

• Ascigil-Dincer, M., & Yilmaz Demirkale, S. (2021). Model development for traffic noise annoyance prediction. Applied Acoustics. DOI: https://doi.org/10.1016/j.apacoust.2021.107909
• Barros, A., Geluykens, M., Pereira, F., Goubert, L., Freitas, E., Vuye, C., (2023). Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise. The Journal of the Acoustical Society of America. DOI: https://doi.org/10.1121/10.0018334
• Barros, A., Vuye, C. (2023). Psychoacoustic indicators of pass-by road traffic noise [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7V904680.
• Botteldooren, D. (2023). Fast noise mapping: A machine learning approach for predicting traffic noise indicators. The Journal of the Acoustical Society of America. DOI: https://doi.org/10.1121/10.0018786
• Fastl, H., & Zwicker, E. (2007). Psychoacoustics: Facts and models (3rd ed.). Berlin: Springer. https://doi.org/10.1007/978-3-540-68888-4
• Gille, L., & Marquis-Favre, C. (2019). Estimation of field psychoacoustic indices and predictive annoyance models for road traffic noise combined with aircraft noise. J Acoust Soc Am. DOI: https://doi.org/10.1121/1.5097573
• John, G., West, G., Lazarescu, & M., (2010). Part Based Recognition of Pedestrians Using Multiple Features and Random Forests. CPS (Conference Publishing Services). DOI: https://espace.curtin.edu.au/handle/20.500.11937/29268
• International Organization for Standardization. (2017). ISO 532-1:2017 – Acoustics — Methods for calculating loudness — Part 1: Zwicker method. Geneva: ISO.
• Leung, T., Chau, C. K., Tang, S. K., Xu, J. (2017). Developing a multivariate model for predicting the noise annoyance responses due to combined water sound and road traffic noise exposure. Applied Acoustics. DOI: https://doi.org/10.1016/j.apacoust.2017.06.020
• Putri, A., Sutoyo, E., Witarsyah, & D., (2019). Hotspots Forecasting Using Autoregressive Integrated Moving Average (ARIMA) for Detecting Forest Fires. 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS). DOI: https://doi.org/10.1109/iotais47347.2019.8980400
• Sheoran, S., Pasari, & S., (2022). Efficacy and application of the window-sliding ARIMA for daily and weekly wind speed forecasting. Journal of Renewable and Sustainable Energy. DOI: https://doi.org/10.1063/5.0108847
• World Health Organization (WHO). (2009). Night noise guidelines for Europe. Copenhagen: WHO Regional Office for Europe. https://www.euro.who.int/en/health-topics/environment-and-health/noise/publications/2009/night-noise-guidelines-for-europe
• World Health Organization (WHO). (2018). Environmental noise guidelines for the European region. Copenhagen: WHO Regional Office for Europe. https://www.who.int/publications/i/item/9789289053563
• Zhou, H., Shu, H., & Song, Y. (2018). Using Machine Learning to Predict Noise-induced Annoyance. TENCON 2018 - 2018 IEEE Region 10 Conference. DOI: https://doi.org/10.1109/tencon.2018.8650341
• Zhu, J., Fang, S., Yang, Z., Qin, Y., Chen, & H., (2023). Prediction of Concrete Strength Based on Random Forest and Gradient Boosting Machine. 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA). DOI: https://doi.org/10.1109/icpeca56706.2023.10075839
• Zwicker, E., & Fastl, H. (1999). Psychoacoustics: Facts and models (2nd ed.). Berlin: Springer. https://doi.org/10.1007/978-3-662-09562-1