{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T06:20:04Z","timestamp":1790490004130,"version":"4.1.0"},"reference-count":65,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:00:00Z","timestamp":1633392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019673","name":"Bilimsel Ara\u015ft\u0131rma Projeleri Birimi, \u0130stanbul Teknik \u00dcniversitesi","doi-asserted-by":"publisher","award":["MOA-2019-42321"],"award-info":[{"award-number":["MOA-2019-42321"]}],"id":[{"id":"10.13039\/501100019673","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may not be stationary, and novel events may exist that eventually deteriorate the performance of the analysis. In this study, a self-learning-based ASA for acoustic event recognition (AER) is presented to detect and incrementally learn novel acoustic events by tackling catastrophic forgetting. The proposed ASA framework comprises six elements: (1) raw acoustic signal pre-processing, (2) low-level and deep audio feature extraction, (3) acoustic novelty detection (AND), (4) acoustic signal augmentations, (5) incremental class-learning (ICL) (of the audio features of the novel events) and (6) AER. The self-learning on different types of audio features extracted from the acoustic signals of various events occurs without human supervision. For the extraction of deep audio representations, in addition to visual geometry group (VGG) and residual neural network (ResNet), time-delay neural network (TDNN) and TDNN based long short-term memory (TDNN\u2013LSTM) networks are pre-trained using a large-scale audio dataset, Google AudioSet. The performances of ICL with AND using Mel-spectrograms, and deep features with TDNNs, VGG, and ResNet from the Mel-spectrograms are validated on benchmark audio datasets such as ESC-10, ESC-50, UrbanSound8K (US8K), and an audio dataset collected by the authors in a real domestic environment.<\/jats:p>","DOI":"10.3390\/s21196622","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5588-577X","authenticated-orcid":false,"given":"Bar\u0131\u015f","family":"Bayram","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-030X","authenticated-orcid":false,"given":"G\u00f6khan","family":"\u0130nce","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul 34469, Turkey"},{"name":"Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","article-title":"Catastrophic interference in connectionist networks: The sequential learning problem","volume":"Volume 24","author":"McCloskey","year":"1989","journal-title":"Psychology of Learning and Motivation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/09540099550039318","article-title":"Catastrophic forgetting, rehearsal and pseudorehearsal","volume":"7","author":"Robins","year":"1995","journal-title":"Connect. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vaila, R., Chiasson, J., and Saxena, V. (2020, January 28\u201330). Continuous Learning in a Single-Incremental-Task Scenario with Spike Features. Proceedings of the International Conference on Neuromorphic Systems 2020, Chicago, IL, USA.","DOI":"10.1145\/3407197.3407213"},{"key":"ref_4","unstructured":"Zhao, H., Wang, H., Fu, Y., Wu, F., and Li, X. (2020). Memory Efficient Class-Incremental Learning for Image Classification. arXiv."},{"key":"ref_5","unstructured":"Yu, L., Liu, X., and van de Weijer, J. (2020). Self-Training for Class-Incremental Semantic Segmentation. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neunet.2019.03.010","article-title":"Continuous learning in single-incremental-task scenarios","volume":"116","author":"Maltoni","year":"2019","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Koh, E., Saki, F., Guo, Y., Hung, C.Y., and Visser, E. (2020, January 6\u201310). Incremental Learning Algorithm For Sound Event Detection. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102859"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Bryan, N.J., Cartwright, M., Bello, J.P., and Salamon, J. (2021, January 6\u201311). Few-Shot Continual Learning for Audio Classification. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413584"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Carletti, V., Foggia, P., Percannella, G., Saggese, A., Strisciuglio, N., and Vento, M. (2013, January 27\u201330). Audio surveillance using a bag of aural words classifier. Proceedings of the 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, Krakow, Poland.","DOI":"10.1109\/AVSS.2013.6636620"},{"key":"ref_10","unstructured":"Naronglerdrit, P., and Mporas, I. Recognition of Indoors Activity Sounds for Robot-Based Home Monitoring in Assisted Living Environments. Proceedings of the International Conference on Interactive Collaborative Robotics."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TASE.2007.911680","article-title":"Robust environmental sound recognition for home automation","volume":"5","author":"Wang","year":"2008","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Saltali, I., Sariel, S., and Ince, G. (2016, January 12\u201316). Scene analysis through auditory event monitoring. Proceedings of the International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents, Tokyo, Japan.","DOI":"10.1145\/3005338.3005343"},{"key":"ref_13","unstructured":"Rivenez12, M., Gorea, A., Pressnitzer, D., and Drake, C. (2002, January 17\u201321). The Tolerance Window for Sequences of Musical, Environmental and Artificial Sounds. Proceedings of the 7th International Conference on Music Perception and Cognition, Sydney, Australia."},{"key":"ref_14","unstructured":"Chu, S., Narayanan, S., and Kuo, C.C.J. (2011). Unstructured environmental audio: Representation, classification and modeling. Machine Audition: Principles, Algorithms and Systems, IGI Global."},{"key":"ref_15","unstructured":"Wyse, L. (2017). Audio spectrogram representations for processing with convolutional neural networks. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 17\u201320). Environmental sound classification with convolutional neural networks. Proceedings of the 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA.","DOI":"10.1109\/MLSP.2015.7324337"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Phan, H., Ch\u00e9n, O.Y., Pham, L., Koch, P., De Vos, M., McLoughlin, I., and Mertins, A. (2019). Spatio-temporal attention pooling for audio scene classification. arXiv.","DOI":"10.21437\/Interspeech.2019-3040"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Waldekar, S., and Saha, G. (2018). Wavelet-Based Audio Features for Acoustic Scene Classification, DCASE Challenge. Tech. Rep.","DOI":"10.21437\/Interspeech.2018-2083"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ford, L., Tang, H., Grondin, F., and Glass, J.R. (2019, January 15\u201319). A Deep Residual Network for Large-Scale Acoustic Scene Analysis. Proceedings of the INTERSPEECH, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2731"},{"key":"ref_20","unstructured":"Kim, C.D., Kim, B., Lee, H., and Kim, G. (2019, January 2\u20137). AudioCaps: Generating captions for audios in the wild. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational 7 Linguistics: Human Language Technologies, Minneapolis, MN, USA. (Long and Short Papers)."},{"key":"ref_21","unstructured":"Palanisamy, K., Singhania, D., and Yao, A. (2020). Rethinking cnn models for audio classification. arXiv."},{"key":"ref_22","unstructured":"Zhou, S., and Beigi, H. (2020). A transfer learning method for speech emotion recognition from automatic speech recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, C.P., Zhang, S.Y., Yeh, C.T., Wang, J.C., Wang, T., and Huang, C.L. (2019, January 12\u201317). Speaker characterization using tdnn-lstm based speaker embedding. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683185"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., Ellis, D.P., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5\u20139). Audio set: An ontology and human-labeled dataset for audio events. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Meire, M., and Karsmakers, P. (2019, January 18\u201321). Comparison of deep autoencoder architectures for real-time acoustic based anomaly detection in assets. Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France.","DOI":"10.1109\/IDAACS.2019.8924301"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Suefusa, K., Nishida, T., Purohit, H., Tanabe, R., Endo, T., and Kawaguchi, Y. (2020, January 4\u20138). Anomalous sound detection based on interpolation deep neural network. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), [Online].","DOI":"10.1109\/ICASSP40776.2020.9054344"},{"key":"ref_27","first-page":"34","article-title":"Application of k-nearest neighbour classification method for intrusion detection in network data","volume":"97","author":"Lakshmi","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/TMM.2011.2122247","article-title":"Probabilistic novelty detection for acoustic surveillance under real-world conditions","volume":"13","author":"Ntalampiras","year":"2011","journal-title":"IEEE Trans. Multimed."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Popescu, M., and Mahnot, A. (2009, January 23\u201327). Acoustic fall detection using one-class classifiers. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/IEMBS.2009.5334521"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"67594","DOI":"10.1109\/ACCESS.2018.2877523","article-title":"Smart audio sensors in the internet of things edge for anomaly detection","volume":"6","author":"Antonini","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., and Lampert, C.H. (2017, January 21\u201326). icarl: Incremental classifier and representation learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref_33","unstructured":"Kemker, R., and Kanan, C. (2017). Fearnet: Brain-inspired model for incremental learning. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 26\u201330). ESC: Dataset for environmental sound classification. Proceedings of the 23rd ACM international conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806390"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Salamon, J., Jacoby, C., and Bello, J.P. (2014, January 3\u20137). A dataset and taxonomy for urban sound research. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2655045"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/TASLP.2017.2690564","article-title":"Improved audio scene classification based on label-tree embeddings and convolutional neural networks","volume":"25","author":"Phan","year":"2017","journal-title":"IEEE ACM Trans. Audio Speech Lang. Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dang, A., Vu, T.H., and Wang, J.C. (2018, January 12\u201314). Acoustic scene classification using convolutional neural networks and multi-scale multi-feature extraction. Proceedings of the 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2018.8326315"},{"key":"ref_38","unstructured":"Zhang, Z., Xu, S., Zhang, S., Qiao, T., and Cao, S. (2020). Learning Frame Level Attention for Environmental Sound Classification. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ciaburro, G., and Iannace, G. (2020). Improving smart cities safety using sound events detection based on deep neural network algorithms. Informatics, 7.","DOI":"10.3390\/informatics7030023"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kataria, S., Nidadavolu, P.S., Villalba, J., Chen, N., Garcia-Perera, P., and Dehak, N. (2020, January 4\u20138). Feature enhancement with deep feature losses for speaker verification. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), [Online].","DOI":"10.1109\/ICASSP40776.2020.9053110"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fathima, N., Patel, T., Mahima, C., and Iyengar, A. (2018, January 2\u20136). TDNN-based Multilingual Speech Recognition System for Low Resource Indian Languages. Proceedings of the INTERSPEECH, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-2117"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, J., Tao, J., Liu, B., Lian, Z., and Niu, M. (2019, January 3\u20136). Efficient modeling of long temporal contexts for continuous emotion recognition. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK.","DOI":"10.1109\/ACII.2019.8925452"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Feng, Z., and Benetos, E. (2019). Adaptive noise reduction for sound event detection using subband-weighted NMF. Sensors, 19.","DOI":"10.3390\/s19143206"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Noh, K., and Chang, J.H. (2020). Joint optimization of deep neural network-based dereverberation and beamforming for sound event detection in multi-channel environments. Sensors, 20.","DOI":"10.3390\/s20071883"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nanni, L., Maguolo, G., Brahnam, S., and Paci, M. (2020). An Ensemble of Convolutional Neural Networks for Audio Classification. arXiv.","DOI":"10.1186\/s13636-020-00175-3"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pandeya, Y.R., Kim, D., and Lee, J. (2018). Domestic cat sound classification using learned features from deep neural nets. Appl. Sci., 8.","DOI":"10.3390\/app8101949"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101084","DOI":"10.1016\/j.ecoinf.2020.101084","article-title":"Data augmentation approaches for improving animal audio classification","volume":"57","author":"Nanni","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Marchi, E., Vesperini, F., Squartini, S., and Schuller, B. (2017). Deep recurrent neural network-based autoencoders for acoustic novelty detection. Comput. Intell. Neurosci.","DOI":"10.1155\/2017\/4694860"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Kirsebom, O.S., Fraz\u00e3o, F., Fablet, R., and Matwin, S. (2019, January 12\u201317). Recurrent neural networks with stochastic layers for acoustic novelty detection. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682901"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nguyen, M.H., Nguyen, D.Q., Nguyen, D.Q., Pham, C.N., Bui, D., and Han, H.D. (2021, January 13\u201315). Deep Convolutional Variational Autoencoder for Anomalous Sound Detection. Proceedings of the 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam.","DOI":"10.1109\/ICCE48956.2021.9352085"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"M\u00fcller, R., Illium, S., Ritz, F., and Schmid, K. (2020). Analysis of Feature Representations for Anomalous Sound Detection. arXiv.","DOI":"10.5220\/0010226800970106"},{"key":"ref_52","unstructured":"Hoang, T.V., Nguyen, H.C., and Pham, G.N. (2020). Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring Using Different Auto-Encoder Methods, DCASE Challenge. Tech. Rep."},{"key":"ref_53","first-page":"3124","article-title":"A comparative study between mfcc and dwt feature extraction technique","volume":"3","author":"Janse","year":"2014","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shimada, K., Koyama, Y., and Inoue, A. (2020, January 4\u20138). Metric learning with background noise class for few-shot detection of rare sound events. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), [Online].","DOI":"10.1109\/ICASSP40776.2020.9054712"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e12564","DOI":"10.1111\/exsy.12564","article-title":"Real time detection of acoustic anomalies in industrial processes using sequential autoencoders","volume":"38","author":"Bayram","year":"2021","journal-title":"Expert Syst."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shi, B., Sun, M., Puvvada, K.C., Kao, C.C., Matsoukas, S., and Wang, C. (2020, January 4\u20138). Few-Shot Acoustic Event Detection Via Meta Learning. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), [Online].","DOI":"10.1109\/ICASSP40776.2020.9053336"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Al-Behadili, H., Grumpe, A., and W\u00f6hler, C. (2015, January 2\u20134). Incremental learning and novelty detection of gestures in a multi-class system. Proceedings of the 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, Sabah, Malaysia.","DOI":"10.1109\/AIMS.2015.55"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shmelkov, K., Schmid, C., and Alahari, K. (2017, January 22\u201329). Incremental learning of object detectors without catastrophic forgetting. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.368"},{"key":"ref_59","unstructured":"Ren, M., Liao, R., Fetaya, E., and Zemel, R. (2019, January 8\u201314). Incremental few-shot learning with attention attractor networks. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, Canada."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Povey, D., Cheng, G., Wang, Y., Li, K., Xu, H., Yarmohammadi, M., and Khudanpur, S. (2018, January 2\u20136). Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks. Proceedings of the INTERSPEECH, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-1417"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yu, Y.Q., and Li, W.J. (2020, January 25\u201329). Densely Connected Time Delay Neural Network for Speaker Verification. Proceedings of the INTERSPEECH 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-1275"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.tins.2004.12.003","article-title":"Memory retention\u2014The synaptic stability versus plasticity dilemma","volume":"28","author":"Abraham","year":"2005","journal-title":"Trends Neurosci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). librosa: Audio and music signal analysis in python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Stowell, D. (2018). Computational bioacoustic scene analysis. Computational Analysis of Sound Scenes and Events, Springer.","DOI":"10.1007\/978-3-319-63450-0_11"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ghaleb, E., Popa, M., and Asteriadis, S. (2019, January 3). Multimodal and temporal perception of audio-visual cues for emotion recognition. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Dublin, Ireland.","DOI":"10.1109\/ACII.2019.8925444"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6622\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:09:35Z","timestamp":1760166575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,5]]},"references-count":65,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196622"],"URL":"https:\/\/doi.org\/10.3390\/s21196622","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,5]]}}}