{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:22:10Z","timestamp":1773098530304,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11042-023-14371-8","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T12:18:22Z","timestamp":1674130702000},"page":"25015-25038","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A fusion way of feature extraction for automatic categorization of music genres"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9656-9714","authenticated-orcid":false,"given":"Dhruv","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9408-9031","authenticated-orcid":false,"given":"Sachin","family":"Taran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-112X","authenticated-orcid":false,"given":"Anukul","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"14371_CR1","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.eswa.2019.06.040","volume":"136","author":"S Abdoli","year":"2019","unstructured":"Abdoli S, Cardinal P, Lameiras Koerich A (2019) End-to-end environmental sound classification using a 1D convolutional neural network. Expert Syst Appl, Elsevier 136:252\u2013263","journal-title":"Expert Syst Appl, Elsevier"},{"key":"14371_CR2","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1007\/s11042-014-2418-z","volume":"75","author":"BK Baniya","year":"2016","unstructured":"Baniya BK, Lee J (2016) Importance of audio feature reduction in automatic music genre classification. Multimed Tools Appl, Springer 75:3013\u20133026","journal-title":"Multimed Tools Appl, Springer"},{"issue":"1","key":"14371_CR3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/21645515.2017.1379639","volume":"14","author":"UA Bhatti","year":"2017","unstructured":"Bhatti UA, Huang M, Wang H, Zhang Y, Mehmood A, Di W (2017) Recommendation system for immunization coverage and monitoring. Human Vacc Immun, Taylor and Francis 14(1):165\u2013171","journal-title":"Human Vacc Immun, Taylor and Francis"},{"issue":"3","key":"14371_CR4","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/17517575.2018.1557256","volume":"13","author":"UA Bhatti","year":"2018","unstructured":"Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2018) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Inform Syst Taylor and Francis 13(3):329\u2013351","journal-title":"Enterprise Inform Syst Taylor and Francis"},{"key":"14371_CR5","doi-asserted-by":"crossref","first-page":"76386","DOI":"10.1109\/ACCESS.2020.2988298","volume":"8","author":"UA Bhatti","year":"2020","unstructured":"Bhatti UA, Yuan L, Yu Z, Li J, Nawaz SA, Mehmood A, Zhang K (2020) Hybrid watermarking algorithm using Clifford algebra with Arnold scrambling and chaotic encryption. IEEE Access 8:76386\u201376398","journal-title":"IEEE Access"},{"key":"14371_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","volume":"60","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, \u2026 Mehmood A (2022) Local similarity-based spatial\u2013spectral fusion hyperspectral image classification with deep CNN and Gabor filtering. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"14371_CR7","doi-asserted-by":"crossref","first-page":"6165","DOI":"10.1007\/s11042-017-4524-1","volume":"77","author":"N Borjian","year":"2018","unstructured":"Borjian N, Kabir E, Seyedin S, Masehian E (2018) A query-by-example music retrieval system using feature and decision fusion. Multimed Tools Appl, Springer 77:6165\u20136189","journal-title":"Multimed Tools Appl, Springer"},{"key":"14371_CR8","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.dsp.2018.03.010","volume":"78","author":"A Boudraa","year":"2018","unstructured":"Boudraa A, Salzenstein F (2018) Teager\u2013Kaiser energy methods for signal and image analysis: a review. Digital Signal Process, Elsevier 78:338\u2013375","journal-title":"Digital Signal Process, Elsevier"},{"key":"14371_CR9","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1177\/0305735619828810","volume":"48","author":"R Brisson","year":"2020","unstructured":"Brisson R, Bianchi R (2020) On the relevance of music genre-based analysis in research on musical tastes. Psychol Music, SAGE J 48:777\u2013794","journal-title":"Psychol Music, SAGE J"},{"key":"14371_CR10","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s00530-021-00886-3","volume":"28","author":"X Cai","year":"2022","unstructured":"Cai X, Zhang H (2022) Music genre classification based on auditory image, spectral and acoustic features. Multimed Syst, Springer 28:779\u2013791","journal-title":"Multimed Syst, Springer"},{"key":"14371_CR11","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/09298215.2020.1761399","volume":"49","author":"A Caparrini","year":"2020","unstructured":"Caparrini A, Arroyo J, P\u00e9rez-Molina L, S\u00e1nchez-Hern\u00e1ndez J (2020) Automatic subgenre classification in an electronic dance music taxonomy. J New Music Res, Taylor and Francis 49:269\u2013284","journal-title":"J New Music Res, Taylor and Francis"},{"key":"14371_CR12","doi-asserted-by":"crossref","first-page":"18801","DOI":"10.1109\/ACCESS.2021.3053864","volume":"9","author":"JR Castillo","year":"2021","unstructured":"Castillo JR, Flores MJ (2021) Web-based music genre classification for timeline song visualization and analysis. IEEE Access 9:18801\u201318816","journal-title":"IEEE Access"},{"key":"14371_CR13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.asoc.2016.12.024","volume":"52","author":"YMG Costa","year":"2017","unstructured":"Costa YMG, Oliveira LS, Silla CN (2017) An evaluation of convolutional neural networks for music classification using spectrograms. Appl Soft Comput J, Elsevier 52:28\u201338","journal-title":"Appl Soft Comput J, Elsevier"},{"key":"14371_CR14","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","volume":"40","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn, IEEE 40:139\u2013157","journal-title":"Mach Learn, IEEE"},{"issue":"20","key":"14371_CR15","doi-asserted-by":"crossref","first-page":"10113","DOI":"10.1073\/pnas.1816414116","volume":"116","author":"KB Doelling","year":"2019","unstructured":"Doelling KB, Assaneo MF, Bevilacqua D, Pesaran B, Poeppel D (2019) An oscillator model better predicts cortical entrainment to music. Proc Natl Acad Sci 116(20):10113\u201310121","journal-title":"Proc Natl Acad Sci"},{"key":"14371_CR16","first-page":"1","volume-title":"Proceedings of the electric electronics","author":"A Elbir","year":"2018","unstructured":"Elbir A, Ilhan HO, Serbes G, Aydin N (2018) Short time Fourier transform based music genre classification. In: Proceedings of the electric electronics. Computer Science, Biomedical Engineerings\u2019 Meeting. IEEE, pp 1\u20134"},{"key":"14371_CR17","doi-asserted-by":"crossref","unstructured":"Ellis DPW, Poliner GE (2007) Identifying `cover songs\u2019 with Chroma features and dynamic programming beat tracking. In proceedings of the IEEE international conference on acoustics, speech and signal processing, 4:1429-1432.","DOI":"10.1109\/ICASSP.2007.367348"},{"key":"14371_CR18","doi-asserted-by":"crossref","first-page":"16003","DOI":"10.1007\/s11042-017-5175-y","volume":"77","author":"S Ferretti","year":"2018","unstructured":"Ferretti S (2018) On the complex network structure of musical pieces: analysis of some use cases from different music genres. Multimed Tools Appl, Springer 77:16003\u201316029","journal-title":"Multimed Tools Appl, Springer"},{"key":"14371_CR19","first-page":"106","volume":"89","author":"JH Foleis","year":"2020","unstructured":"Foleis JH, Tavares TF (2020) Texture selection for automatic music genre classification. Appl Soft Comput J, Elsevier 89:106\u2013127","journal-title":"Appl Soft Comput J, Elsevier"},{"key":"14371_CR20","doi-asserted-by":"crossref","unstructured":"Fredriksson D (2019) Pathways of pop: arts and education policy, studief\u00f6rbund and genre hierarchies. In: Marija Dumni\u0107 Vilotijevi\u0107, Ivana Medi\u0107 (Ed) contemporary Popular Music studies, 19th edition, springer VS, Wiesbaden, Germany.","DOI":"10.1007\/978-3-658-25253-3_10"},{"key":"14371_CR21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TMM.2010.2098858","volume":"13","author":"Z Fu","year":"2011","unstructured":"Fu Z, Lu G, Ting KM, Zhang D (2011) A survey of audio-based music classification and annotation. IEEE Trans Multimedia 13:303\u2013319","journal-title":"IEEE Trans Multimedia"},{"key":"14371_CR22","unstructured":"Haggblade M, Hong Y, Rao K (2011) Music Genre Classification. Stanford University, pp:1\u20135.(online) (https:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.375.204&rep=rep1&type=pdf)"},{"key":"14371_CR23","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/TASL.2007.909434","volume":"16","author":"A Holzapfel","year":"2008","unstructured":"Holzapfel A, Stylianou Y (2008) Musical genre classification using nonnegative matrix factorization-based features. IEEE Trans Audio Speech Lang Process 16:424\u2013434","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"14371_CR24","doi-asserted-by":"crossref","unstructured":"Jain U, Nathani K, Ruban N et al (2018) Cubic SVM classifier based feature extraction and emotion detection from speech signals. In proceedings of the 2018 international conference on sensor networks and signal processing. IEEE, 386\u2013391","DOI":"10.1109\/SNSP.2018.00081"},{"key":"14371_CR25","doi-asserted-by":"crossref","unstructured":"Jha CK, Kolekar MH (2020) Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier. Biomedical signal processing and control, Elsevier 59(101875).","DOI":"10.1016\/j.bspc.2020.101875"},{"key":"14371_CR26","doi-asserted-by":"crossref","unstructured":"Kaiser JF (1990) On a simple algorithm to calculate the \u201cenergy\u201d of a signal. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp:381\u2013384","DOI":"10.1109\/ICASSP.1990.115702"},{"key":"14371_CR27","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/ICASSP.1993.319457","volume":"3","author":"JF Kaiser","year":"1993","unstructured":"Kaiser JF (1993) Some useful properties of Teager\u2019s energy operators. IEEE Int Conf Acoustics Speech Signal Process 3:149\u2013152","journal-title":"IEEE Int Conf Acoustics Speech Signal Process"},{"issue":"2","key":"14371_CR28","first-page":"19","volume":"6","author":"PU Kiran","year":"2018","unstructured":"Kiran PU, Abhiram N, Taran S, Bajaj V (2018) TQWT based features for classification of ALS and healthy EMG signals. Am J Compt Sci Inform Technol 6(2):19","journal-title":"Am J Compt Sci Inform Technol"},{"key":"14371_CR29","doi-asserted-by":"crossref","first-page":"17071","DOI":"10.1007\/s11042-022-12254-y","volume":"81","author":"B Kumaraswamy","year":"2022","unstructured":"Kumaraswamy B (2022) Optimized deep learning for genre classification via improved moth flame algorithm. Multimedia Tools Appl, Springer 81:17071\u201317093","journal-title":"Multimedia Tools Appl, Springer"},{"key":"14371_CR30","doi-asserted-by":"crossref","unstructured":"Kumaraswamy B, Poonacha PG (2021) Deep convolutional neural network for musical genre classification via new self Adaptive Sea lion optimization. Applied soft computing, Elsevier, 108.","DOI":"10.1016\/j.asoc.2021.107446"},{"key":"14371_CR31","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1109\/LSP.2017.2713830","volume":"24","author":"J Lee","year":"2017","unstructured":"Lee J, Nam J (2017) Multi-level and multi-scale feature aggregation using Pretrained convolutional neural networks for music auto-tagging. IEEE Signal Process Lett 24:1208\u20131212","journal-title":"IEEE Signal Process Lett"},{"key":"14371_CR32","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.artmed.2008.03.002","volume":"43","author":"MC Lee","year":"2008","unstructured":"Lee MC, Nelson SJ (2008) Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy. Artif Intell Med, Elsevier 43:61\u201374","journal-title":"Artif Intell Med, Elsevier"},{"key":"14371_CR33","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TMM.2009.2017635","volume":"11","author":"CH Lee","year":"2009","unstructured":"Lee CH, Shih JL, Yu KM, Lin HS (2009) Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features. IEEE Trans Multimedia 11:670\u2013682","journal-title":"IEEE Trans Multimedia"},{"key":"14371_CR34","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.marstruc.2018.03.013","volume":"60","author":"CB Li","year":"2018","unstructured":"Li CB, Choung J, Noh M-H (2018) Wide-banded fatigue damage evaluation of catenary mooring lines using various artificial neural networks models. Marine Struct, Elsevier 60:186\u2013200","journal-title":"Marine Struct, Elsevier"},{"key":"14371_CR35","doi-asserted-by":"crossref","first-page":"4621","DOI":"10.1007\/s11042-020-10465-9","volume":"81","author":"J Li","year":"2022","unstructured":"Li J, Han L, Li X, \u2026 Gou Z (2022) An evaluation of deep neural network models for music classification using spectrograms. Multimed Tools Appl, Springer 81:4621\u20134647","journal-title":"Multimed Tools Appl, Springer"},{"key":"14371_CR36","doi-asserted-by":"crossref","first-page":"10337","DOI":"10.1007\/s00521-022-06896-0","volume":"34","author":"J Li","year":"2022","unstructured":"Li J, Han L, Wang Y, \u2026 Yan H (2022) Combined angular margin and cosine margin softmax loss for music classification based on spectrograms. Neural Comput Appl, Springer 34:10337\u201310353","journal-title":"Neural Comput Appl, Springer"},{"key":"14371_CR37","doi-asserted-by":"crossref","first-page":"7313","DOI":"10.1007\/s11042-020-09643-6","volume":"80","author":"C Liu","year":"2021","unstructured":"Liu C, Feng L, Liu G, \u2026 Liu S (2021) Bottom-up broadcast neural network for music genre classification. Multimedia Tools Appl, Springer 80:7313\u20137331","journal-title":"Multimedia Tools Appl, Springer"},{"key":"14371_CR38","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/ACCESS.2014.2333095","volume":"2","author":"K Markov","year":"2014","unstructured":"Markov K, Matsui T (2014) Music genre and emotion recognition using Gaussian processes. IEEE Access 2:688\u2013697","journal-title":"IEEE Access"},{"key":"14371_CR39","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/09298215.2018.1438476","volume":"47","author":"L Nanni","year":"2018","unstructured":"Nanni L, Costa YMG, Aguiar RL, \u2026 Brahnam S (2018) Ensemble of deep learning, visual and acoustic features for music genre classification. J New Music Res, Taylor and Francis 47:383\u2013397","journal-title":"J New Music Res, Taylor and Francis"},{"key":"14371_CR40","doi-asserted-by":"crossref","first-page":"152713","DOI":"10.1109\/ACCESS.2020.3017661","volume":"8","author":"WWY Ng","year":"2020","unstructured":"Ng WWY, Zeng W, Wang T (2020) Multi-level local feature coding fusion for music genre recognition. IEEE Access 8:152713\u2013152727","journal-title":"IEEE Access"},{"key":"14371_CR41","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/TASLP.2014.2355774","volume":"22","author":"Y Panagakis","year":"2014","unstructured":"Panagakis Y, Kotropoulos CL, Arce GR (2014) Music genre classification via joint sparse low-rank representation of audio features. IEEE\/ACM Trans Audio Speech Language Process 22:1905\u20131917","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"issue":"3","key":"14371_CR42","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/CJECE.2020.2970144","volume":"43","author":"N Pelchat","year":"2020","unstructured":"Pelchat N, Gelowitz CM (2020) Neural network music genre classification. Can J Electr Comput Eng 43(3):170\u2013173","journal-title":"Can J Electr Comput Eng"},{"key":"14371_CR43","doi-asserted-by":"crossref","first-page":"22509","DOI":"10.1007\/s11042-020-09890-7","volume":"80","author":"M Pichl","year":"2021","unstructured":"Pichl M, Zangerle E (2021) User models for multi-context-aware music recommendation. Multimed Tools Appl, Springer 80:22509\u201322531","journal-title":"Multimed Tools Appl, Springer"},{"key":"14371_CR44","volume-title":"Latent feature extraction for musical genres from raw audio","author":"A Sawhney","year":"2018","unstructured":"Sawhney A, Vasavada V, Wang W (2018) Latent feature extraction for musical genres from raw audio. Stanford University"},{"key":"14371_CR45","doi-asserted-by":"crossref","first-page":"3560","DOI":"10.1109\/TSP.2011.2143711","volume":"59","author":"IW Selesnick","year":"2011","unstructured":"Selesnick IW (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59:3560\u20133575","journal-title":"IEEE Trans Signal Process"},{"key":"14371_CR46","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1016\/j.sigpro.2011.03.019","volume":"91","author":"JS Seo","year":"2011","unstructured":"Seo JS, Lee S (2011) Higher-order moments for musical genre classification. Signal Process, Elsevier 91:2154\u20132157","journal-title":"Signal Process, Elsevier"},{"key":"14371_CR47","doi-asserted-by":"crossref","unstructured":"Sugianto S, Suyanto S (2019) Voting-Based Music Genre Classification Using Melspectogram and Convolutional Neural Network. In Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, pp:330\u2013333","DOI":"10.1109\/ISRITI48646.2019.9034644"},{"issue":"2","key":"14371_CR48","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1177\/1754073914558282","volume":"7","author":"S Swaminathan","year":"2015","unstructured":"Swaminathan S, Schellenberg EG (2015) Current emotion research in music psychology. Emotion Rev, Sage 7(2):189\u2013197","journal-title":"Emotion Rev, Sage"},{"key":"14371_CR49","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TASLP.2013.2287052","volume":"22","author":"H Tachibana","year":"2014","unstructured":"Tachibana H, Ono N, Sagayama S (2014) Singing voice enhancement in monaural music signals based on two-stage harmonic\/percussive sound separation on multiple resolution spectrograms. IEEE\/ACM Trans Audio, Speech Language Process 22:228\u2013237","journal-title":"IEEE\/ACM Trans Audio, Speech Language Process"},{"key":"14371_CR50","doi-asserted-by":"crossref","first-page":"6925","DOI":"10.1007\/s00521-018-3531-0","volume":"31","author":"S Taran","year":"2019","unstructured":"Taran S, Bajaj V (2019) Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Comput Appl, Springer 31:6925\u20136932","journal-title":"Neural Comput Appl, Springer"},{"key":"14371_CR51","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1109\/TASSP.1980.1163453","volume":"28","author":"HM Teager","year":"1980","unstructured":"Teager HM (1980) Some observations on oral airflow during phonation. IEEE Trans. Acoustics, Speech, Signal Process 28:599\u2013601","journal-title":"IEEE Trans. Acoustics, Speech, Signal Process"},{"key":"14371_CR52","first-page":"73","volume-title":"A phenomenological model for vowel production in the vocal tract","author":"HM Teager","year":"1983","unstructured":"Teager HM, Teager SM (1983) A phenomenological model for vowel production in the vocal tract. Speech Science, Recent Advances, pp 73\u2013109"},{"key":"14371_CR53","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TSA.2002.800560","volume":"10","author":"G Tzanetakis","year":"2002","unstructured":"Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10:293\u2013302","journal-title":"IEEE Trans Speech Audio Process"},{"key":"14371_CR54","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TNNLS.2015.2498149","volume":"28","author":"Y Wang","year":"2017","unstructured":"Wang Y, Zhang W, Wu L, \u2026 Zhao X (2017) Unsupervised metric fusion over Multiview data by graph random walk-based cross-view diffusion. IEEE Trans Neural Networks Learn Syst 28:57\u201370","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"14371_CR55","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2019.09.054","volume":"372","author":"Y Yu","year":"2020","unstructured":"Yu Y, Luo S, Liu S, \u2026 Feng L (2020) Deep attention-based music genre classification. Neurocomputing, Elsevier 372:84\u201391","journal-title":"Neurocomputing, Elsevier"},{"key":"14371_CR56","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TVT.2019.2949603","volume":"69","author":"Q Zou","year":"2020","unstructured":"Zou Q, Jiang H, Dai Q, \u2026 Wang Q (2020) Robust lane detection from continuous driving scenes using deep neural networks. IEEE Trans Veh Technol 69:41\u201354","journal-title":"IEEE Trans Veh Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14371-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14371-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14371-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T18:44:04Z","timestamp":1687545844000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14371-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":56,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["14371"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14371-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]},"assertion":[{"value":"13 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"To the best of our knowledge, this work does not have any financial and\/or non-financial conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}