{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T11:10:06Z","timestamp":1747653006029,"version":"3.40.5"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11760-025-04031-9","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:27:59Z","timestamp":1743769679000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of various continuous wavelet transforms for acoustic scene classification with DCASE dataset"],"prefix":"10.1007","volume":"19","author":[{"given":"Vikash Kumar","family":"Singh","sequence":"first","affiliation":[]},{"given":"Kalpana","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Samarendra Nath","family":"Sur","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"4031_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120520","volume":"229","author":"VK Singh","year":"2023","unstructured":"Singh, V.K., Sharma, K., Sur, S.N.: A survey on preprocessing and classification techniques for acoustic scene. Expert Syst. with Appl. 229, 120520 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120520","journal-title":"Expert Syst. with Appl."},{"key":"4031_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107790","volume":"159","author":"X Fan","year":"2020","unstructured":"Fan, X., Sun, T., Chen, W., Fan, Q.: Deep neural network based environment sound classification and its implementation on hearing aid app. Measurement 159, 107790 (2020). https:\/\/doi.org\/10.1016\/j.measurement.2020.107790","journal-title":"Measurement"},{"issue":"11","key":"4031_CR3","doi-asserted-by":"publisher","first-page":"7911","DOI":"10.1007\/s11042-019-08279-5","volume":"79","author":"S Waldekar","year":"2020","unstructured":"Waldekar, S., Saha, G.: Analysis and classification of acoustic scenes with wavelet transform-based MEL-scaled features. Multimed. Tools Appl. 79(11), 7911\u20137926 (2020)","journal-title":"Multimed. Tools Appl."},{"key":"4031_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.5540\/tcam.2021.022.01.00139","volume":"22","author":"F Gossler","year":"2021","unstructured":"Gossler, F., Oliveira, B., Duarte, M., Vieira Filho, J., Villarreal, F., Lambl\u00e9m, R.: Gaussian and golden wavelets: a comparative study and their applications in structural health monitoring. Trends Comput. Appl. Math. 22, 139\u2013155 (2021)","journal-title":"Trends Comput. Appl. Math."},{"issue":"1","key":"4031_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32604\/sdhm.2021.012751","volume":"15","author":"A Silik","year":"2021","unstructured":"Silik, A., Noori, M., Altabey, W.A., Ghiasi, R., Wu, Z.: Comparative analysis of wavelet transform for time-frequency analysis and transient localization in structural health monitoring. Struct. Durab. Health Monit. 15(1), 1 (2021)","journal-title":"Struct. Durab. Health Monit."},{"key":"4031_CR6","unstructured":"Mesaros, A., Heittola, T., Virtanen, T.: A multi-device dataset for urban acoustic scene classification (2018) arXiv:1807.09840 [eess.AS]"},{"key":"4031_CR7","unstructured":"Heittola, T., Mesaros, A., Virtanen, T.: Acoustic scene classification in dcase 2020 challenge: generalization across devices and low complexity solutions (2020) arXiv:2005.14623 [eess.AS]"},{"key":"4031_CR8","volume-title":"A wavelet tour of signal processing","author":"S Mallat","year":"1999","unstructured":"Mallat, S.: A wavelet tour of signal processing. Academic Press, London (1999)"},{"key":"4031_CR9","volume-title":"Wavelets: algorithms & applications","author":"Y Meyer","year":"1993","unstructured":"Meyer, Y.: Wavelets: algorithms & applications. SIAM (Society for Industrial and Applied Mathematics), Philadelphia (1993)"},{"key":"4031_CR10","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104","volume-title":"Ten lectures on wavelets: Society for industrial and applied mathematics","author":"I Daubechies","year":"1992","unstructured":"Daubechies, I.: Ten lectures on wavelets: Society for industrial and applied mathematics. PA, USA (1992)"},{"issue":"10","key":"4031_CR11","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1002\/cpa.21413","volume":"65","author":"S Mallat","year":"2012","unstructured":"Mallat, S.: Group invariant scattering. Commun. Pure Appl. Math. 65(10), 1331\u20131398 (2012)","journal-title":"Commun. Pure Appl. Math."},{"issue":"16","key":"4031_CR12","doi-asserted-by":"publisher","first-page":"4114","DOI":"10.1109\/TSP.2014.2326991","volume":"62","author":"J And\u00e9n","year":"2014","unstructured":"And\u00e9n, J., Mallat, S.: Deep scattering spectrum. IEEE Trans. Signal Process. 62(16), 4114\u20134128 (2014)","journal-title":"IEEE Trans. Signal Process."},{"issue":"8","key":"4031_CR13","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"J Bruna","year":"2013","unstructured":"Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872\u20131886 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4031_CR14","unstructured":"And\u00e9n, J., Mallat, S.: Multiscale scattering for audio classification. In: ISMIR, pp. 657\u2013662. Miami, Florida (2011)"},{"issue":"4","key":"4031_CR15","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.3390\/s22041535","volume":"22","author":"V Hajihashemi","year":"2022","unstructured":"Hajihashemi, V., Gharahbagh, A.A., Cruz, P.M., Ferreira, M.C., Machado, J.J.M., Tavares, J.M.R.S.: Binaural acoustic scene classification using wavelet scattering, parallel ensemble classifiers and nonlinear fusion. Sensors 22(4), 1535 (2022). https:\/\/doi.org\/10.3390\/s22041535","journal-title":"Sensors"},{"key":"4031_CR16","doi-asserted-by":"publisher","unstructured":"Peng, C., Cheng, W., Song, Z., Dong, R.: A noise-robust modulation signal classification method based on continuous wavelet transform. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 745\u2013750 (2020). https:\/\/doi.org\/10.1109\/ITOEC49072.2020.9141879","DOI":"10.1109\/ITOEC49072.2020.9141879"},{"issue":"1","key":"4031_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TMECH.2021.3058061","volume":"27","author":"H Shao","year":"2021","unstructured":"Shao, H., Xia, M., Wan, J., Silva, C.W.: Modified stacked autoencoder using adaptive Morlet wavelet for intelligent fault diagnosis of rotating machinery. IEEE\/ASME Trans. Mechatron. 27(1), 24\u201333 (2021)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"issue":"04","key":"4031_CR18","doi-asserted-by":"publisher","first-page":"2250012","DOI":"10.1142\/S1793524522500127","volume":"15","author":"Z Sabir","year":"2022","unstructured":"Sabir, Z., Umar, M., Raja, M.A.Z., Baskonus, H.M., Gao, W.: Designing of Morlet wavelet as a neural network for a novel prevention category in the HIV system. Int. J. Biomath. 15(04), 2250012 (2022)","journal-title":"Int. J. Biomath."},{"issue":"05","key":"4031_CR19","doi-asserted-by":"publisher","first-page":"2240147","DOI":"10.1142\/S0218348X22401478","volume":"30","author":"B Wang","year":"2022","unstructured":"Wang, B., Gomez-Aguilar, J., Sabir, Z., Raja, M.A.Z., Xia, W.-F., Jahanshahi, H., Alassafi, M.O., Alsaadi, F.E.: Numerical computing to solve the nonlinear corneal system of eye surgery using the capability of Morlet wavelet artificial neural networks. Fractals 30(05), 2240147 (2022)","journal-title":"Fractals"},{"issue":"3","key":"4031_CR20","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/LCOMM.2020.3039251","volume":"25","author":"Z Cui","year":"2020","unstructured":"Cui, Z., Gao, Y., Hu, J., Tian, S., Cheng, J.: LOS\/NLOS identification for indoor UWB positioning based on Morlet wavelet transform and convolutional neural networks. IEEE Commun. Lett. 25(3), 879\u2013882 (2020)","journal-title":"IEEE Commun. Lett."},{"key":"4031_CR21","first-page":"299","volume":"2022","author":"A Singh","year":"2022","unstructured":"Singh, A., Rawat, A., Raghuthaman, N.: Mexican hat wavelet transform and its applications. Methods Math. Modell. Comput. Complex Syst. 2022, 299\u2013317 (2022)","journal-title":"Methods Math. Modell. Comput. Complex Syst."},{"key":"4031_CR22","doi-asserted-by":"publisher","unstructured":"Singh, V.K., Sharma, K., Sur, S.N.: Development of acoustic scene classification model using neural networks applied on reduced dataset of DCASE. In: 2023 9th International Conference on Signal Processing and Communication (ICSC), pp. 544\u2013550 (2023). https:\/\/doi.org\/10.1109\/ICSC60394.2023.10441179","DOI":"10.1109\/ICSC60394.2023.10441179"},{"key":"4031_CR23","unstructured":"Mart\u00edn-Morat\u00f3, I., Heittola, T., Mesaros, A., Virtanen, T.: Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 challenge systems (2021) arXiv:2105.13734 [eess.AS]"},{"key":"4031_CR24","unstructured":"Kim, B.: Building light-weight convolutional neural networks for acoustic scene classification using audio embeddings. Technical report, DCASE2021 Challenge (2021)"},{"key":"4031_CR25","unstructured":"Phan, D., Jones, D.: DCASE 2021 task 1 subtask a: Low-complexity acoustic scene classification. Technical report, DCASE2021 Challenge (2021)"},{"key":"4031_CR26","unstructured":"Kim, M., Shin, S., Baek, S., Lee, S., Park, S., Jeong, Y.: Acoustic scene classification with decomposed convolution neural networks. Technical report, DCASE2021 Challenge (2021)"},{"key":"4031_CR27","unstructured":"Puy, G., Jain, H., Bursuc, A.: Separable convolutions and test-time augmentations for low-complexity and calibrated acoustic scene classification. Technical report, DCASE2021 Challenge (June 2021)"},{"key":"4031_CR28","unstructured":"Hee-Soo, H., Jee-weon, J., Hye-jin, S., Bong-Jin, L.: Clova submission for the DCASE 2021 challenge: Acoustic scene classification using light architectures and device augmentation. Technical report, DCASE2021 Challenge (2021)"},{"key":"4031_CR29","unstructured":"Cai, Y., Lin, M., Li, S., Shao, X.: DCASE 2024 task1 submission: Data-efficient acoustic scene classification with self-supervised teachers. Tech. Rep, Technical report, DCASE Challenge (2024)"},{"key":"4031_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.111452","volume":"199","author":"L Wang","year":"2022","unstructured":"Wang, L., Wei, Y., Wang, Y., Chen, Q., Liu, P., Chai, X.: Research on comprehensive and effective acoustic signal processing methods for caculating downhole liquid level depth. Measurement 199, 111452 (2022)","journal-title":"Measurement"},{"issue":"1","key":"4031_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e03243","volume":"6","author":"N Kumar","year":"2020","unstructured":"Kumar, N., Kumar, R.: Wavelet transform-based multipitch estimation in polyphonic music. Heliyon 6(1), e03243 (2020)","journal-title":"Heliyon"},{"key":"4031_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103612","volume":"80","author":"N Kumar","year":"2021","unstructured":"Kumar, N., Kumar, R., Murmu, G., Sethy, P.K.: Extraction of melody from polyphonic music using modified Morlet wavelet. Microprocess. Microsyst. 80, 103612 (2021)","journal-title":"Microprocess. Microsyst."},{"key":"4031_CR33","volume-title":"Multiresolution signal decomposition: transforms, subbands, and wavelets","author":"AN Akansu","year":"2001","unstructured":"Akansu, A.N., Haddad, R.A.: Multiresolution signal decomposition: transforms, subbands, and wavelets. Academic Press, London (2001)"},{"key":"4031_CR34","volume-title":"MATLAB for neuroscientists: an introduction to scientific computing in MATLAB","author":"P Wallisch","year":"2014","unstructured":"Wallisch, P., Lusignan, M.E., Benayoun, M.D., Baker, T.I., Dickey, A.S., Hatsopoulos, N.G.: MATLAB for neuroscientists: an introduction to scientific computing in MATLAB. Academic Press, London (2014)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04031-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04031-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04031-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T10:36:04Z","timestamp":1747650964000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04031-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,2]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["4031"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04031-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,4,2]]},"assertion":[{"value":"19 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"440"}}