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Audio classification with Neural Networks for IoT implementation
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för elektronikkonstruktion.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

This project is based upon two previous projects handed to the author by the Norwegian University of Science and Technology in co-operation with Disruptive Technologies.

 

The report discusses sound sensing and Neural Networks, and their application in IoT. The goal was to determine what type of Neural Networks or classification methods was most suited for audio classification. This was done by applying various classification methods and Neural Networks on a data set consisting of 8732 sound samples. These methods where logistic regression, Feed-Forward Neural Network, Convolutional Neural Network, Gated Recurrent Unit, and Long Short-term Memory network. To compare the Neural Networks the accuracy of the training data set and the validation data set were evaluated. Out of these methods the feed-forward network yielded the highest validation accuracy and is the preferable classification method. However, with more work and refinement the Long Short-term memory may prove to be the better solution.

 

Future work with a Vesper V1010 piezoelectric microphone and IoT implementation is discussed, as well as the social and ethical difficulties proposed by what is essentially a data gathering system.

sted, utgiver, år, opplag, sider
2019. , s. 40
Emneord [en]
Neural Networks, deep learning, machine learning, acoustics, sound sensor, IoT, statistical classifiers
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-37640Lokal ID: EL-V19-A2-042OAI: oai:DiVA.org:miun-37640DiVA, id: diva2:1368469
Fag / kurs
Electronics EL1
Utdanningsprogram
Masterprogram i elektroniksystem och instrumentation TEIAA 120 AV
Veileder
Examiner
Tilgjengelig fra: 2019-11-07 Laget: 2019-11-07 Sist oppdatert: 2019-11-07bibliografisk kontrollert

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