prikaz prve stranice dokumenta Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu
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undergraduate thesis
Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:943439
Haramustek, Zvonimir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Haramustek, Z. (2023). Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:943439

Haramustek, Zvonimir. "Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:943439

Haramustek, Zvonimir. "Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:943439

Haramustek, Z. (2023). 'Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:943439

Haramustek Z. Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 July 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:943439

Z. Haramustek, "Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:943439

Metadata
TitleRaspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu
Title (english)Classification of Acoustic Events Using Recurrent Neural Network on Embedded System
AuthorZvonimir Haramustek
MentorDinko Oletić (mentor)
Committee memberDinko Oletić (predsjednik povjerenstva)
Committee memberStjepan Begušić (član povjerenstva)
Committee memberMarko Šimić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-06-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Raspoznavanje vrsta akustičkih događaja ponavljajućom neuronskom mrežom na ugradbenom sustavu. Ispitane su različite veličine LSTM ponavljajuće neuronske mreže te nekoliko veličina Mel frekvencijskih značajki korištenih kao ulaz neuronske mreže u svrhe detekcije zvuka motorne pile s potencijalnom primjenom u automatskom nadziranju velikih površina šuma kako bi se detektiralo i suzbilo ilegalnu deforestaciju. Dobiveni model prilagođen je ST Microelectronics mikrokontroleru STM32L4R5ZI s X-CUBE-AI programskim paketom te su na njemu ispitane performanse i memorijsko zauzeće različitih neuronskih mreža te računanje značajki pomoću CMSIS-DSP programskog paketa u stvarnom vremenu iz signala MEMS mikrofona čiji je izlaz PDM signal koji se demodulira pomoću DFSDM sklopovlja mikrokontrolera. Dobiveni su rezultati od 90 do 94,7% točnosti te memorijsko zauzeće od 32 KB do 2 MB ovisno o značajkama ulaza i arhitekturi te je postignuto izvođenje u stvarnom vremenu.
Abstract (english)
Recognition of types of acoustic events by a recurrent neural network on an embedded system. Different sizes of LSTM recurrent neural network and several sizes of Mel frequency features used as neural network input for chainsaw sound detection purposes with potential application in automatic monitoring of large forest areas to detect and suppress illegal deforestation were tested. The resulting model was adapted to the ST Microelectronics microcontroller STM32L4R5ZI with the X-CUBE-AI program package, and the performance and memory usage of different neural networks were tested on it, as well as the calculation of features using the CMSIS-DSP program package in real time from the MEMS microphone signal, the output of which is a PDM signal that is demodulated using the DFSDM circuit of the microcontroller. Results of 90 to 94.7% accuracy and memory usage of 32 KB to 2MB depending on input features and architecture were obtained, and real-time execution was achieved.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:943439
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:06:18