{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:19:01Z","timestamp":1785424741103,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62261056"],"award-info":[{"award-number":["62261056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62261056"],"award-info":[{"award-number":["62261056"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Qinghai Minzu University Key Scientific Research Project in Science and Engineering","award":["2024XJZD01"],"award-info":[{"award-number":["2024XJZD01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Edge-side acoustic monitoring enables animal sound recognition in remote environments, but microcontroller deployment remains constrained by feature extraction, numerical consistency, memory, latency, and energy consumption. This study presents a sensor-based tiny machine learning (TinyML) acoustic monitoring system on an Arduino Nano 33 BLE Sense Rev2 platform, integrating onboard pulse-density modulation (PDM) microphone acquisition, Mel-frequency cepstral coefficient (MFCC) feature extraction, deployment-side standardization, 8-bit integer (INT8) neural-network inference, and edge-side decision output. To reduce training-to-deployment feature drift, consistent frame parameters, mirrored C++ feature operators, and exported standardization parameters are used to align personal-computer-side and microcontroller-side feature representations. A source-isolated seven-class protocol was constructed for six target animal classes and one compound background-noise class. In the single-run baseline comparison, the proposed multilayer perceptron achieved 98.28% test accuracy and 97.21% test macro-F1, while the ten-seed stability analysis yielded 98.64% \u00b1 0.26% test accuracy and 97.87% \u00b1 0.38% test macro-F1. The deployed INT8 model occupied approximately 26.9 KB, with a post-window latency of about 303 ms. System-level input power was 0.783\u20130.825 W, corresponding to an estimated autonomy of 7.63\u20138.03 h under the reference battery setting.<\/jats:p>","DOI":"10.3390\/s26133972","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Sensor-Based TinyML Acoustic Monitoring System for Edge-Side Animal Sound Recognition on Resource-Constrained Microcontrollers"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0951-9882","authenticated-orcid":false,"given":"Zhiqing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2865-567X","authenticated-orcid":false,"given":"Guicai","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"ref_1","first-page":"1298","article-title":"Current condition and protection strategies of Qinghai-Tibet Plateau ecological security barrier","volume":"36","author":"Fu","year":"2021","journal-title":"Bull. Chin. Acad. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/biosci\/biy147","article-title":"Terrestrial passive acoustic monitoring: Review and perspectives","volume":"69","author":"Sugai","year":"2019","journal-title":"BioScience"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/09524622.2023.2209052","article-title":"Passive acoustic monitoring in terrestrial vertebrates: A review","volume":"32","author":"Hoefer","year":"2023","journal-title":"Bioacoustics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21966","DOI":"10.1038\/s41598-022-26429-y","article-title":"ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning","volume":"12","author":"Bergler","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e13152","DOI":"10.7717\/peerj.13152","article-title":"Computational bioacoustics with deep learning: A review and roadmap","volume":"10","author":"Stowell","year":"2022","journal-title":"PeerJ"},{"key":"ref_6","first-page":"800","article-title":"TensorFlow Lite Micro: Embedded machine learning for TinyML systems","volume":"3","author":"David","year":"2021","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1016\/j.jksuci.2021.11.019","article-title":"A review on TinyML: State-of-the-art and prospects","volume":"34","author":"Ray","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schizas, N., Karras, A., Karras, C., and Sioutas, S. (2022). TinyML for ultra-low power AI and large scale IoT deployments: A systematic review. Future Internet, 14.","DOI":"10.3390\/fi14120363"},{"key":"ref_9","unstructured":"(2026, May 07). Google AI Edge. LiteRT for Microcontrollers. Available online: https:\/\/ai.google.dev\/edge\/litert\/microcontrollers\/overview."},{"key":"ref_10","unstructured":"Banbury, C., Reddi, V.J., Torelli, P., Holleman, J., Jeffries, N., Kiraly, C., Montino, P., Kanter, D., Ahmed, S., and Pau, D. (2021). MLPerf Tiny Benchmark. arXiv."},{"key":"ref_11","unstructured":"Arduino (2026, May 07). Arduino Nano 33 BLE Sense Rev2 Datasheet. Available online: https:\/\/docs.arduino.cc\/resources\/datasheets\/ABX00069-datasheet.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_13","unstructured":"(2026, May 07). Librosa Development Team. Librosa.Feature.Mfcc. Available online: https:\/\/librosa.org\/doc\/main\/generated\/librosa.feature.mfcc.html."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ko\u015bmider, M. (2020, January 25\u201329). Spectrum correction: Acoustic scene classification with mismatched recording devices. Proceedings of the INTERSPEECH, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-3088"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101236","DOI":"10.1016\/j.ecoinf.2021.101236","article-title":"BirdNET: A deep learning solution for avian diversity monitoring","volume":"61","author":"Kahl","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15733","DOI":"10.1038\/s41598-021-95076-6","article-title":"Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture","volume":"11","author":"Hossain","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1038\/s41559-023-02148-z","article-title":"Limits to the accurate and generalizable use of soundscapes to monitor biodiversity","volume":"7","author":"Sethi","year":"2023","journal-title":"Nat. Ecol. Evol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102043","DOI":"10.1016\/j.ecoinf.2023.102043","article-title":"An integrated system for the acoustic monitoring of goat farms","volume":"75","author":"Ntalampiras","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_19","unstructured":"Rabiner, L.R., and Schafer, R.W. (2011). Theory and Applications of Digital Speech Processing, Pearson."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Schulthess, L., Marty, S., Dirodi, M., Rocha, M.D., R\u00fcttimann, L., Hahnloser, R.H.R., and Magno, M. (2023, January 21\u201325). TinyBird-ML: An ultra-low power smart sensor node for bird vocalization analysis and syllable classification. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA.","DOI":"10.1109\/ISCAS46773.2023.10181431"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/103162.103163","article-title":"What every computer scientist should know about floating-point arithmetic","volume":"23","author":"Goldberg","year":"1991","journal-title":"ACM Comput. Surv."},{"key":"ref_22","unstructured":"(2026, May 07). Scikit-learn Developers. Sklearn.Preprocessing.StandardScaler. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.StandardScaler.html."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/2382577.2382579","article-title":"Leakage in data mining: Formulation, detection, and avoidance","volume":"6","author":"Kaufman","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102851","DOI":"10.1016\/j.ecoinf.2024.102851","article-title":"Improving acoustic species identification using data augmentation within a deep learning framework","volume":"83","author":"MacIsaac","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, E., Moon, J., Shim, J., and Hwang, E. (2023). DualDiscWaveGAN-based data augmentation scheme for animal sound classification. Sensors, 23.","DOI":"10.3390\/s23042024"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sarris, A.L., Vryzas, N., Vrysis, L., and Dimoulas, C. (2024). Investigation of data augmentation techniques in environmental sound recognition. Electronics, 13.","DOI":"10.3390\/electronics13234719"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1525\/bio.2011.61.3.6","article-title":"Soundscape ecology: The science of sound in the landscape","volume":"61","author":"Pijanowski","year":"2011","journal-title":"BioScience"},{"key":"ref_29","unstructured":"TensorFlow (2026, May 07). Post-Training Quantization. Available online: https:\/\/www.tensorflow.org\/model_optimization\/guide\/quantization\/post_training."},{"key":"ref_30","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","unstructured":"(2026, May 07). Scikit-learn Developers. Metrics and Scoring: Quantifying the Quality of Predictions. Available online: https:\/\/scikit-learn.org\/stable\/modules\/model_evaluation.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/3972\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:14:11Z","timestamp":1782263651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/3972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26133972"],"URL":"https:\/\/doi.org\/10.3390\/s26133972","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]}}}