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Review of Emergency Vehicle Detection Techniques by Acoustic Signals

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Abstract

The performance of emergency vehicle prioritization system is determined by its efficiency in reducing the response time of the vehicles detected. Emergency vehicle (EV) detection is possible through various methods, but the use of the acoustic signal in detection method is found to have an edge over the others. The present study reviewed various acoustic-based EV detection systems and their merits, which would provide a better understanding of techniques to be used for IoT system design with limited power and computational resources. It is found that EV siren detection accuracy decreases in low SNR conditions and is affected by the choice of features used in neural network (NN). It is observed that neural network-based system performance is better compared to other methods. Also, it is observed that network parameters of long short-term memory recurrent NN architecture are almost 150 time less as compared to other NN for similar detection accuracy. More research on acoustic-based systems is required to be done, for achieving high detection accuracy of 99% in low SNR condition of − 15 dB or below. Developing a universally deployable generalized model of a neural network in detecting EV siren and designing a system of low power with low computation requirements are the main goals which are analyzed in the present study. In detecting EV by acoustic-based method, the effects of noise, various signal domain features, environment, relative movement of source and detector, etc. have been studied here. The physical characteristics of the siren signal are analyzed and how these can be used in emergency vehicle detection systems are discussed. The present study analyzes the acoustic-based EV detection system into three major categories, namely digital signal processing-based systems, neural network-based systems, and statistical methods-based systems. For all the methods discussed in these categories, the research gaps are identified to indicate future research directions. Further, major challenges and future scope in the acoustic-based EV detection system are presented. Also, the new direction to increase the accuracy and decrease the latency of the EV detection system is discussed.

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Availability of Data and Materials

The datasets, figure used during and/or analyzed during the current study are available from the open source research paper of author available.

Abbreviations

ASR:

Automatic speech recognition

PCEN:

Per-channel energy normalization

MAP:

Maximum a posteriori

CNN:

Convolutional neural networks

K-NN:

K-nearest neighbor

ANN:

Artificial neural network

BRANN:

Bayesian regularized artificial neural network.

GMM:

Gaussian mixture models

SVM:

Support vector machine

NN:

Neural network

FFT:

Fast Fourier transform

MDF:

Module difference function

ADSR:

Attack–decay–sustain–release

µPa:

Micro-Pascal

SED:

Sound event detection

ILD:

Interaural level difference

ITD:

Interaural time difference

EVP:

Emergency vehicle priority

AV:

Autonomous vehicle

EV:

Emergency vehicle

LCS:

Longest common subsequence

LSTM-RNN:

Long short-term memory recurrent neural network

MFCC:

Mel-frequency cepstral coefficients

References

  • Angela DP (2013) Emergency vehicle siren noise effectiveness. Master Thesis. University of Windsor Ontario, Canada.

  • Angione F, Novak C, Imeson C, Lehman A, Merwin B, Pagliarella T, Samardzic N, D’Angela P, Ule H (2016) Study of a low-frequency emergency siren in comparison to traditional siren technology. Proc Mtgs Acoust 29(1):030008

    Article  Google Scholar 

  • Aviva Atkins February (2020) Speech enhancement based on adaptive line enhancer. Master Thesis. Israel Institute of Technology

  • Bubar A, Eckstein B, Ell A, Hilts E, Martin S, Powell T, Miguel Cruz A, Rios Rincon A (2020) Emergency siren detection technology and hearing impairment: a systematized literature review. Disabil Rehabil 18:295

    Google Scholar 

  • Carmel D, Yeshurun A, Moshe Y (2017) Detection of alarm sounds in noisy environments. In: IEEE 25th European Signal Processing Conference (EUSIPCO), pp 1839–1843

  • Chavdar M, Gerazov B, Ivanovski Z, Kartalov T (2020) Towards a system for automatic traffic sound event detection. In: IEEE 28th Telecommunications Forum (TELFOR), pp 1–4

  • De Lorenzo RA, Eilers MA (1991) Lights and siren: a review of emergency vehicle warning systems. Ann Emerg Med 20(12):1331–1335

    Article  Google Scholar 

  • Dobre RA, Negrescu C, Stanomir D (2017) Improved low computational method for siren detection. In: IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), pp 318–323

  • Dubin S, Zietz S (2004) Personal alerting technology for people with hearing difficulty: pilot study. In: IEEE Proceedings of the 30th Annual Northeast Bioengineering Conference, pp 202–203

  • Ebizuka Y, Kato S, Itami M 2019 Detecting approach of emergency vehicles using siren sound processing. In: IEEE Intelligent Transportation Systems Conference (ITSC), pp 4431–4436

  • Fatimah, B, Preethi A, Hrushikesh V, Singh A and Kotion H.R 2020 An automatic siren detection algorithm using Fourier decomposition method and MFCC. In: IEEE 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1–6

  • Howard CQ, Maddern AJ, Privopoulos EP (2011) Acoustic characteristics for effective ambulance sirens. Acoust Aust 39(2):43–53

    Google Scholar 

  • Ick C, McFee B 2021 June. Sound event detection in urban audio with single and multi-rate PCEN. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 880–884

  • Jonnadula EP, Khilar PM (2021) Comparison of various techniques for emergency vehicle detection using audio processing. In: Gupta BB (ed) Cloud security. CRC Press, Boca Raton, pp 64–75

    Chapter  Google Scholar 

  • Kiran SL, Supriya M (2017) Siren detection and driver assistance using modified minimum mean square error method. In: IEEE International Conference on Smart Technologies for Smart Nation (SmartTechCon), pp 127–131

  • Lilja AP, Raboshchuk G, Nadeu C (2017) A neural network approach for automatic detection of acoustic alarms. In: Biosignals, pp 84–91

  • Marchegiani L, Posner I (2017) Leveraging the urban soundscape: Auditory perception for smart vehicles. In: IEEE International Conference on Robotics and Automation (ICRA), pp 6547–6554

  • Marchegiani L, Newman P (2022) listening for sirens: Locating and classifying acoustic alarms in city scenes. IEEE Trans Intell Transp Syst 23(10):17087–17096

    Article  Google Scholar 

  • Mehendale N, Patel R, Mange S, Mulik S (2021) Review of emergency vehicle priority systems. SSRN. https://doi.org/10.2139/ssrn.3859424

    Article  Google Scholar 

  • Mesaros A, Heittola T, Virtanen T, Plumbley MD (2021) Sound event detection: a tutorial. IEEE Signal Process Mag 38(5):67–83

    Article  Google Scholar 

  • Meucci F, Pierucci L, Del Re E. Lastrucci L and Desii P (2008) A real-time siren detector to improve safety of guide in traffic environment. In: IEEE 16th European Signal Processing Conference, pp 1–5

  • Miyazakia T, Kitazonoa Y, Shimakawab M (2013) Ambulance siren detector using FFT on dsPIC. In: Proceedings of the 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing, pp 266–269

  • Montoro L, Drastrup JK, Johan M (2017) Automatic detection of emergency cars. Master Thesis, Aalborg University.

  • Müller M (2015) Music Representations, Fundamentals of music processing: Audio, analysis, algorithms, applications. Springer (Audio Representation)

    Book  Google Scholar 

  • Padhy S, Tiwari J, Rathore S, Kumar N (2019) Emergency signal classification for the hearing impaired using multi-channel convolutional neural network architecture. In: IEEE Conference on Information and Communication Technology, pp 1–6

  • Palecek J, Cerny M (2016) Emergency horn detection using embedded systems. In: IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp 257–261

  • Park SW, Trevino J (2013) Automatic detection of emergency vehicles for hearing impaired drivers. Texas a&m Univ-Kingsv EE/CS Dep MSC 192:637–665

    Google Scholar 

  • Rane D, Shirodkar P, Panigrahi T, Mini S (2019) Detection of ambulance siren in traffic. In: IEEE International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp 401–405

  • Raval P, Christopher J (2021) Parameter tuning for wavelet-based sound event detection using neural networks. International conference on computational intelligence in music, sound, art and design (Part of EvoStar). Springer, Cham, pp 235–247

    Google Scholar 

  • Rudzyn B, Kadous W, Sammut C (2007) Real time robot audition system incorporating both 3D sound source localization and voice characterization. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 4733–4738

  • Schröder J, Goetze S, Grützmacher V, Anemüller J (2013) Automatic acoustic siren detection in traffic noise by part-based models. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 493–497

  • Scola CF, Ortega MDB (2010) Direction of arrival estimation: a two microphones approach. Master Thesis. Blekinge Institute of Technology.

  • Shabtai NR, Tzirke E (2019) Detecting the direction of emergency vehicle sirens with microphones. In: EAA Spatial Audio Signal Processing Symposium, pp 137–142

  • Srinivasamurthy KM, Angadi VJ, Kubrin SP, Matteppanavar S, Kumar PM, Rudraswamy B (2018) Evidence of enhanced ferromagnetic nature and hyperfine interaction studies of Ce-Sm doped Co-Ni ferrite nanoparticles for microphone applications. Ceram Int 44(15):18878–18885

    Article  Google Scholar 

  • Sun H, Liu X, Xu K, Miao J, Luo Q (2021) Emergency vehicles audio detection and localization in autonomous driving. arXiv preprint arXiv:2109.14797

  • Supreeth H V, Rao S, Chethan KS, Purushotham U (2020) Identification of Ambulance Siren sound and Analysis of the signal using statistical method. In: IEEE International Conference on Intelligent Engineering and Management (ICIEM), pp 198–202

  • The Goertzel Algorithm (2022). https://courses.cs.washington.edu/courses/cse466/12au/calendar/Goertzel-EETimes.pdf. Accessed 06 Jan 2022

  • The Human Ear Hearing, Sound Intensity and Loudness Levels (2022). https://courses.physics.illinois.edu/phys406/sp2017/Lecture_Notes/P406POM_Lecture_Notes/P406POM_Lect5.pdf. Accessed 03 March 2022

  • TranYVT, Tsai YC, Ho W (2018) Detection of ambulance and fire truck siren sounds using neural networks. In: Proceedings of 51st Research World International Conference, pp 9–14

  • Tran VT, Tsai WH (2020) Acoustic-based emergency vehicle detection using convolutional neural networks. IEEE Access 8:75702–75713

    Article  Google Scholar 

  • Wang Y, Getreuer P, Hughes T, Lyon RF, Saurous RA (2017) Trainable frontend for robust and far-field keyword spotting. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 5670–5674

  • ‘What are the Different Sounds a Police Siren Makes (2022). https://www.extremetacticaldynamics.com/blog/what-are-the-different-sounds-a-policesiren-makes/. Accessed 10 April 2022

  • When to Use the Goertzel Algorithm Instead of the FFT? (2022).https://medium.com/geekculture/the-great-unknown-the-goertzel-algorithm-492681f30866. Accessed 02 Feb 2022

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Authors and Affiliations

  1. Electronics & IT, Nehru Science Centre Mumbai, Dr E Moses Road Worli, Mumbai, Maharashtra, 400018, India

    Krishnendu Choudhury & Dalia Nandi

  2. Electronics and Communication, Indian Institute of Information Technology Kalyani, Kalyani Webel IT Park, Kalyani, India

    Krishnendu Choudhury

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  1. Krishnendu Choudhury
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  2. Dalia Nandi
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Conceptualization and writing original draft: KC; Supervision, review and editing: DN. Authors reviewed the final manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Krishnendu Choudhury.

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Choudhury, K., Nandi, D. Review of Emergency Vehicle Detection Techniques by Acoustic Signals. Trans Indian Natl. Acad. Eng. 8, 535–550 (2023). https://doi.org/10.1007/s41403-023-00424-9

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