Skip to main content
SpringerLink
Log in

Vehicle Speed Recognition from Noise Spectral Patterns

  • Research paper
  • Published:
International Journal of Environmental Research Aims and scope Submit manuscript

Abstract

The use of noise maps in large cities has become a common practice since they have been recognized as a powerful tool for determining the population exposure to environmental noise and, therefore, a valid aid to identify the most appropriate mitigation actions. The possibility to complement noise maps with further information such as the vehicle speed distribution, can represent an additional benefit to local Municipalities. The well-known fact that speed is the most relevant characteristic to discriminate between different vehicle noise spectra, led us to perform a dedicated measuring campaign devoted to record both vehicle features (noise spectrum and speed). The recorded spectra have been statistically analyzed and classified according to their speeds. Three main spectral patterns, corresponding to different mean speed, have been derived. Such patterns have been used, first, for discriminating among non-vehicle noises and, second, they have been associated with vehicles travelling within a certain speed interval. The results suggest that this method can prove useful in practical situations in which traffic noise and vehicle mobility need to be controlled/assessed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

Journal article

  • Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    Google Scholar 

  • Averbuch A, Rabin N, Schclar A, Zheludev V (2012) Dimensionality reduction for detection of moving vehicles. Pattern Anal Appl 15(1):19–27

    Article  Google Scholar 

  • Ballesteros JA, Sarradj E, Fernández MD, Geyer T, Ballesteros MJ (2015) Noise source identification with Beamforming in the pass-by of a car. Appl Acoust 93:106–119

    Article  Google Scholar 

  • Brock G, Pihur V, Datta S, Datta S (2008) clValid: an R package for cluster validation. J Stat Softw 25(4):1–22

    Article  Google Scholar 

  • Can A, Gauvreau B (2015) Describing and classifying urban sound environments with a relevant set of physical indicators. J Acoust Soc Am 137:208–218

    Article  CAS  Google Scholar 

  • Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  Google Scholar 

  • Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis and density estimation. J Am Stat Assoc 97(458):611–631

    Article  Google Scholar 

  • Glegg S, Yoon J (1990) Determination of noise source height, Part I: the measurement of equivalent acoustic source height above a reflecting surface. J Sound Vib 143(1):19–37

    Article  Google Scholar 

  • Hartigan JA, Wong MA (1979) A K-means clustering algorithm. Appl Stat 28(1):100–108

    Article  Google Scholar 

  • Romberg J, Candes E, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  Google Scholar 

  • Kakar VK, Kandpal M (2013) Techniques of acoustic feature extraction for detection and classification of ground vehicles. Int J Emerg Technol Adv Eng 3(2):419–426

    Google Scholar 

  • Savage R (1961) Probability inequalities of the Tchebycheff type. J Res Natl Bur Stand B Math Math Phys 65B:3

    Google Scholar 

  • Sharan RV, Moir TJ (2016) An overview of applications and advancements in automatic sound recognition. Neurocomputing 200:22–34. doi:10.1016/j.neucom.2016.03.020

    Article  Google Scholar 

  • Smiraglia M, Benocci R, Zambon G, Roman HE (2016) Predicting hourly traffic noise from traffic flow rate model: underlying concepts for the DYNAMAP project. Noise Mapp 3(1):130–139

    Google Scholar 

  • Torija JA, Ruiz DP (2016) Automated classification of urban locations for environmental noise impact assessment on the basis of road-traffic content. Expert Syst Appl 53:1–13

    Article  Google Scholar 

  • Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244

    Article  Google Scholar 

  • Wu H, Siegel M, Khosla P (1999) Vehicle sound signature recognition by frequency vector principal component analysis. IEEE Trans Instrum Meas 48(5):1005–1009

    Article  Google Scholar 

  • Zambon G, Benocci R, Bisceglie A, Roman HE, Bellucci P (2016a) The LIFE DYNAMAP project: towards a procedure for dynamic noise mapping in urban areas. Appl Acoust. doi:10.1016/j.apacoust.2016.10.022

    Google Scholar 

  • Zambon G, Benocci R, Brambilla G (2016b) Cluster categorization of urban roads to optimize their noise monitoring. Environ Monit Assess 188(1):26 (1–11)

    Article  CAS  Google Scholar 

  • Zambon G, Benocci R, Brambilla G (2016c) Statistical road classification applied to stratified spatial sampling of road traffic noise in urban areas. Int J Environ Res 10(3):411–420

    Google Scholar 

  • Zambon G, Roman HE, Benocci R (2017) Scaling model for a speed-dependent vehicle noise spectrum. J Traffic Transp Eng (English Edition) 4(3):230–239. doi:10.1016/j.jtte.2017.05.001

    Article  Google Scholar 

  • Zuo F, Li Y, Johnson S, Johnson J, Varughese S, Copes R, Liu F, Wu HJ, Hou R, Chen H (2014) Temporal and spatial variability of traffic-related noise in the City of Toronto, Canada. Sci Total Environ 472:1100–1107

    Article  CAS  Google Scholar 

Book, authored

  • Kaufman K, Rousseeuw P (1990) Finding groups in data. Wiley, Hoboken

    Book  Google Scholar 

  • Sandberg U, Ejsmont JA (2002) Tyre/road noise reference book. INFORMEX Ejsmont & Sandberg Handelsbolag Harg, Sweden

    Google Scholar 

Paper presented at a conference

  • Alías F, Socoró JC, Sevillano X, Nencini L (2015) Training an anomalous noise event detection algorithm for dynamic road traffic noise mapping: environmental noise recording campaign. In: Proceedings of the 46th national congress on acoustics, Valencia, Spain, 21–23 October

  • Benocci R, Roman HR, Zambon G, Radaelli S (2014) Traffic acoustical classes based upon vehicle characteristics. In: Proceedings of forum acusticum, Krakow, Poland, 7–12 September

  • Socoró JC, Albiol X, Sevillano X, Alías F (2016) Analysis and automatic detection of anomalous noise events in real recordings of road traffic noise for the LIFE DYNAMAP project. In: Proceedings of the 45th international congress and exposition on noise control engineering, Hamburg, Germany, 21–24 August

  • Socoró JC, Ribera G, Sevillano X, Alías F (2015) Development of an anomalous noise event detection algorithm for dynamic road traffic noise mapping. In: Proceedings of the 22nd international congress on sound and vibration, Florence, Italy, 12–16 July

  • Wang W, Qi H (2002) Acoustic target classification using distributed sensor arrays. In: Proceedings of IEEE ICASSP, Orlando, FL, USA, 13–17 May

  • Zambon G, Benocci R, Brambilla G, Gallo V (2014) Statistics-based functional classification of roads in the urban area of Milan. In: Proceedings of forum acusticum, Krakow, Poland, 7–12 September

  • Zambon G, Angelini F, Benocci R, Bisceglie A, Radaelli S, Coppi P, Bellucci P, Giovannetti A, Grecco R (2015) DYNAMAP: a new approach to real-time noise mapping. In: Proceedings of EuroNoise 2015, Maastricht, Holland, 31 May–3 June

Internet publication/Online document

Technical Reports

  • Fraley C, Raftery AE, Murphy TB, Scrucca L (2012) mclust version 4 for R: Normal Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation. Tech. Rep. No. 597, Department of Statistics, University of Washington, USA

  • General Motors North American Engineering Standards (2004) Road tire noise evaluation procedure. Test procedure GMN7079TP, General Motors Corporation, Detroit, MI, USA

  • Hald J (1995) Use of spatial transformation of sound fields (STSF) techniques in the automotive industry. Bruel & Kjaer, Technical Review, 1, Nrerum, Denmark

  • ISO 362–1 (2007) Measurement of noise emitted by accelerating road vehicles—engineering method—Part 1: M and N categories. ISO, Geneva

    Google Scholar 

  • Tsaig Y, Donoho DL, Drori I, Strack JL (2006) Sparse solution of underdetermined linear equations by stage wise orthogonal machine pursuit, Technical report N. 2006-2, Department of Statistics, Stanford University, USA

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Scienze dell’Ambiente e del Territorio e di Scienze della Terra, Università degli Studi di Milano–Bicocca, P.zza della Scienza 1, 20126, Milan, Italy

    G. Zambon & R. Benocci

  2. Dipartimento di Fisica “G. Occhialini”, Università degli Studi di Milano–Bicocca, P.zza della Scienza 3, 20126, Milan, Italy

    H. E. Roman

Authors
  1. G. Zambon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. E. Roman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Benocci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Benocci.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zambon, G., Roman, H.E. & Benocci, R. Vehicle Speed Recognition from Noise Spectral Patterns. Int J Environ Res 11, 449–459 (2017). https://doi.org/10.1007/s41742-017-0040-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41742-017-0040-4

Keywords

Search

Navigation