{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:22:17Z","timestamp":1783610537287,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe Research and innovation programme","award":["101070374"],"award-info":[{"award-number":["101070374"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows\u2019 variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples.<\/jats:p>","DOI":"10.3390\/a17100440","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:08:47Z","timestamp":1727780927000},"page":"440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Impact of Sliding Window Variation and Neuronal Time Constants on Acoustic Anomaly Detection Using Recurrent Spiking Neural Networks in Automotive Environment"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8624-5353","authenticated-orcid":false,"given":"Shreya","family":"Kshirasagar","sequence":"first","affiliation":[{"name":"Robert Bosch GmbH (Corporate Research), 71272 Renningen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4144-0283","authenticated-orcid":false,"given":"Andre","family":"Guntoro","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH (Corporate Research), 71272 Renningen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-0872","authenticated-orcid":false,"given":"Christian","family":"Mayr","sequence":"additional","affiliation":[{"name":"Highly-Parallel VLSI Systems and Neuro-Microelectronics, Technische Universit\u00e4t Dresden, 01062 Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel\u2019s Loihi","volume":"Volume 23","author":"Polykretis","year":"2020","journal-title":"Proceedings of the International Conference on Neuromorphic Systems 2020 (ICONS 2020)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7388","DOI":"10.1109\/CVPR.2017.781","article-title":"A low power, fully event-based gesture recognition system","volume":"Volume 1","author":"Amir","year":"2017","journal-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8941","DOI":"10.1038\/ncomms9941","article-title":"An event-based architecture for solving constraint satisfaction problems","volume":"6","author":"Mostafa","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1038\/s42256-021-00311-4","article-title":"Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes","volume":"3","author":"Maass","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Custode, L.L., Mo, H., Ferigo, A., and Iacca, G. (2022). Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction. Algorithms, 15.","DOI":"10.3390\/a15030098"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Blouw, P., Choo, X., Hunsberger, E., and Eliasmith, C. (2019, January 26\u201328). Benchmarking keyword spotting efficiency on neuromorphic hardware. Proceedings of the NICE \u201919: Proceedings 7th Annual Neuro-Inspired Computational Elements Workshop, New York, NY, USA.","DOI":"10.1145\/3320288.3320304"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kshirasagar, S., Cramer, B., Guntoro, A., and Mayr, C. (2024, January 22\u201325). Auditory Anomaly Detection using Recurrent Spiking Neural Networks. Proceedings of the 2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/AICAS59952.2024.10595878"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dominguez-Morales, J.P., Liu, Q., James, R., Gutierrez-Galan, D., Jimenez-Fernandez, A., Davidson, S., and Furber, S. (2018, January 8\u201313). Deep spiking neural network model for time-variant signals classification: A real-time speech recognition approach. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489381"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MM.2018.112130359","article-title":"Loihi: A Neuromorphic Manycore Processor with On-Chip Learning","volume":"38","author":"Davies","year":"2018","journal-title":"IEEE Micro"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2014.2304638","article-title":"The SpiNNaker project","volume":"102","author":"Furber","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1146\/annurev.neuro.27.070203.144247","article-title":"The Neural Basis of Temporal Processing","volume":"27","author":"Mauk","year":"2004","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_12","unstructured":"Fazenda, B., Atmoko, H., Gu, F., Guan, L., and Ball, A. (2009, January 18\u201321). Acoustic based safety emergency vehicle detection for intelligent transport systems. Proceedings of the ICCAS-SICE 2009: ICROS-SICE International Joint Conference, Fukuoka, Japan."},{"key":"ref_13","unstructured":"Carmel, D., Yeshurun, A., and Moshe, Y. (September, January 28). Detection of alarm sounds in noisy environments. Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"75702","DOI":"10.1109\/ACCESS.2020.2988986","article-title":"Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks","volume":"8","author":"Tran","year":"2020","journal-title":"IEEE Access."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cantarini, M., Brocanelli, A., Gabrielli, L., and Squartini, S. (2021, January 13\u201315). Acoustic Features for Deep Learning-Based Models for Emergency Siren Detection: An Evaluation Study. Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia.","DOI":"10.1109\/ISPA52656.2021.9552140"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17087","DOI":"10.1109\/TITS.2022.3158076","article-title":"Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes","volume":"23","author":"Marchegiani","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1126\/science.1254642","article-title":"A million spiking-neuron integrated circuit with a scalable communication network and interface","volume":"345","author":"Merolla","year":"2014","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/s41597-022-01727-2","article-title":"Large-scale audio dataset for emergency vehicle sirens and road noises","volume":"9","author":"Asif","year":"2022","journal-title":"Sci. Data"},{"key":"ref_19","unstructured":"Stowell, D., Pamu\u026ba, H., and Wood, M. (2018). Evaluation datasets for DCASE 2018 Bird Audio Detection (1.0) [Data set]. Zenodo."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.7717\/peerj-cs.1052","article-title":"Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models","volume":"8","author":"Fernandes","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.inffus.2019.06.013","article-title":"Adaptive sliding window based activity recognition for assisted livings","volume":"53","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, G., Li, Q., Wang, L., Wang, W., Wu, M., and Liu, T. (2018). Impact of sliding window length in indoor human motion modes and pose pattern recognition based on smartphone sensors. Sensors, 18.","DOI":"10.3390\/s18061965"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.neuron.2007.05.005","article-title":"The sodium \u201cleak\u201d has finally been plugged","volume":"54","author":"Snutch","year":"2007","journal-title":"Neuron"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.neuron.2011.12.007","article-title":"Sodium leak channels in neuronal excitability and rhythmic behaviors","volume":"72","author":"Ren","year":"2011","journal-title":"Neuron"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bouanane, M.S., Cherifi, D., Chicca, E., and Khacef, L. (2023). Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1244675"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2021.07.091","article-title":"Towards understanding the effect of leak in spiking neural networks","volume":"464","author":"Chowdhury","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","unstructured":"Kshirasagar, S., Guntoro, A., and Mayr, C. (2024, January 17\u201319). An Empirical Evaluation of Sliding Windows on Siren Detection Task Using Spiking Neural Networks. Proceedings of the 6th International Conference on Advances in Signal Processing and Artificial Intelligence, Madeira Island, Portugal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","article-title":"Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks","volume":"36","author":"Neftci","year":"2019","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_30","unstructured":"Pehle, C., and Pedersen, J.E. (2019). Norse: A library to do deep learning with spiking neural networks. GitHub."},{"key":"ref_31","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. 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