{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T18:09:11Z","timestamp":1774807751924,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,18]],"date-time":"2018-11-18T00:00:00Z","timestamp":1542499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).<\/jats:p>","DOI":"10.3390\/s18114019","type":"journal-article","created":{"date-parts":[[2018,11,22]],"date-time":"2018-11-22T09:18:25Z","timestamp":1542878305000},"page":"4019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data"],"prefix":"10.3390","volume":"18","author":[{"given":"Yunbin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6470-3341","authenticated-orcid":false,"given":"Jaewon","family":"Sa","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-2959","authenticated-orcid":false,"given":"Sungju","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7953","DOI":"10.3390\/s150407953","article-title":"Fast Video Encryption Using the H. 264 Error Propagation Property for Smart Mobile Devices","volume":"15","author":"Chung","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, S., and Jeong, T. (2017). Forecasting Purpose Data Analysis and Methodology Comparison of Neural Model Perspective. Symmetry, 9.","DOI":"10.3390\/sym9070108"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14647","DOI":"10.3390\/s121114647","article-title":"Energy Efficient Image\/video Data Transmission on Commercial Multi-core Processors","volume":"12","author":"Lee","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20170227","DOI":"10.1587\/elex.14.20170227","article-title":"Real-time Processing for Intelligent-surveillance Applications","volume":"14","author":"Lee","year":"2017","journal-title":"IEICE Electron. Express"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, S., and Jeong, T. (2016). Cloud-based Parameter-driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment. Symmetry, 8.","DOI":"10.3390\/sym8100103"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ribeiro, C., Ferworn, A., Denko, M., and Tran, J. (2009, January 25\u201327). Canine Pose Estimation: A Computing for Public Safety Solution. Proceedings of the 2009 Canadian Conference on Computer and Robot Vision, Kelowna, BC, Canada.","DOI":"10.1109\/CRV.2009.38"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.tvjl.2008.12.010","article-title":"Barking in Family Dogs: An Ethological Approach","volume":"183","year":"2010","journal-title":"Vet. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.5713\/ajas.2012.12628","article-title":"Automatic Detection of Cow\u2019s Estrus in Audio Surveillance System","volume":"26","author":"Chung","year":"2013","journal-title":"Asian-Australas. J. Anim. Sci."},{"key":"ref_9","unstructured":"Ye, L., and Keogh, E. (July, January 28). Time Series Shapelets: A New Primitive for Data Mining. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","article-title":"LSTM Fully Convolutional Networks for Time Series Classification","volume":"6","author":"Karim","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lukman, A., Harjoko, A., and Yang, C.K. (2017, January 26\u201328). Classification MFCC Feature from Culex and Aedes Aegypti Mosquitoes Noise Using Support Vector Machine. Proceedings of the 2017 International Conference on Soft Computing, ICSIIT, Denpasar, Indonesia.","DOI":"10.1109\/ICSIIT.2017.28"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.bspc.2016.10.004","article-title":"Heart sound classification based on scaled spectrogram and partial least squares regression","volume":"32","author":"Zhang","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dong, M. (arXiv, 2018). Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification, arXiv.","DOI":"10.32470\/CCN.2018.1153-0"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sonawane, A., Inamdar, M.U., and Bhangale, K.B. (2017, January 17\u201319). Sound based human emotion recognition using MFCC & multiple SVM. Proceedings of the International Conference on Information, Communication, Instrumentation and Control (ICICIC), Indore, India.","DOI":"10.1109\/ICOMICON.2017.8279046"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, C., Ahn, J., Ko, Y., Park, J., and Gallagher, J.C. (2017, January 13\u201315). Real-time UAV sound detection and analysis system. Proceedings of the 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, NJ, USA.","DOI":"10.1109\/SAS.2017.7894058"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.scitotenv.2016.11.004","article-title":"Mapping the Birch and Grass Pollen Seasons in the UK Using Satellite Sensor Time-series","volume":"578","author":"Khwarahm","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vitola, J., Pozo, F., Tibaduiza, D.A., and Anaya, M. (2017). A Sensor Data Fusion System Based on K-nearest Neighbor Pattern Classification for Structural Health Monitoring Applications. Sensors, 17.","DOI":"10.3390\/s17020417"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, J., Fu, Y., Ming, J., Ren, Y., Sun, L., and Xiong, H. (2017, January 13\u201317). Effective and Real-time In-app Activity Analysis in Encrypted Internet Traffic Streams. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098049"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., He, K., Li, J., and Geng, Y. (2017, January 11\u201314). Seq2Img: A Sequence-to-image Based Approach towards IP Traffic Classification Using Convolutional Neural Networks. Proceedings of the 2017 IEEE Conference on Big Data, Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258054"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C.L.Y., and Courville, A. (arXiv, 2017). Towards End-to-end Speech Recognition with Deep Convolutional Neural Networks, arXiv.","DOI":"10.21437\/Interspeech.2016-1446"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TNNLS.2017.2651018","article-title":"Multivariate Time-series Classification Using the Hidden-unit Logistic Model","volume":"29","author":"Pei","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.asoc.2017.12.032","article-title":"Ensemble of Evolving Data Clouds and Fuzzy Models for Weather Time Series Prediction","volume":"64","author":"Soares","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Manandhar, S., Dev, S., Lee, Y.H., Meng, Y.S., and Winkler, S. (arXiv, 2018). A Data-driven Approach to Detecting Precipitation from Meteorological Sensor Data, arXiv.","DOI":"10.1109\/IGARSS.2018.8519275"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/JPHOTOV.2016.2626919","article-title":"A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data from Multiple Heterogeneous Photovoltaic Test Sites","volume":"7","author":"Hu","year":"2017","journal-title":"IEEE J. Photovolt."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Garcke, J., Iza-Teran, R., Marks, M., Pathare, M., Schollbach, D., and Stettner, M. (2017, January 19). Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines. Proceedings of the Scientific Computing and Algorithms in Industrial Simulations, Cham, Switzerland.","DOI":"10.1007\/978-3-319-62458-7_16"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e1392","DOI":"10.1002\/wics.1392","article-title":"Data Representation for Time Series Data Mining: Time Domain Approaches","volume":"9","author":"Wilson","year":"2017","journal-title":"WIREs Comput. Stat."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Egri, A., Horv\u00e1th, I., Kov\u00e1cs, F., Molontay, R., and Varga, K. (2017, January 20\u201323). Cross-correlation Based Clustering and Dimension Reduction of Multivariate Time Series. Proceedings of the 2017 IEEE 21st International Conference on Intelligent Engineering Systems, Larnaca, Cyprus.","DOI":"10.1109\/INES.2017.8118563"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201317). Data Augmentation of Wearable Sensor Data for Parkinson\u2019s Disease Monitoring Using Convolutional Neural Networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, New York, NY, USA.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, Z.H., Kittler, J., Christmas, W., Huber, P., and Wu, X.J. (2017, January 21\u201326). Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.392"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1006\/jmbi.2000.4315","article-title":"Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes","volume":"305","author":"Krogh","year":"2001","journal-title":"J. Mol. Biol."},{"key":"ref_32","unstructured":"Berndt, D.J., and Clifford, J. (August, January 31). Using Dynamic Time Warping to Find Patterns in Time Series. Proceedings of the KDD Workshop, Seattle, WA, USA."},{"key":"ref_33","unstructured":"Adafruit (2018, July 06). Measuring Sound Levels. Available online: https:\/\/learn.adafruit.com\/adafruit-microphone-amplifier-breakout\/measuring-sound-levels."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/4019\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:29Z","timestamp":1760196629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/4019"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,18]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18114019"],"URL":"https:\/\/doi.org\/10.3390\/s18114019","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,18]]}}}