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Classify different types of boat engine sounds with machine learning
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

When a boat moves in water, it creates a sound with unique features which makes it possible to identify different boat types or even a specific boat. The ability to identify boats is important in the military sector for surveillance purposes.This thesis describes how different audio processing methods and machine learning approaches are implemented, tested and evaluated in order to create a prototype that identifies boats. A total of 87 boat sounds were used and processed in seven different ways. The machine learning approaches Dense Neural Network, Convolutional Neural Network and Recurrent Neural Network were implemented and trained with the processed audio files in order to identify different boat types. Different combinations of audio processing methods and machine learning approaches ability to classify different boat types, were tested with a stratified Kfold test.The result is a prototype with an audio processing method that divides an audio file to equally large segments. Each segment is converted to a logarithmic mel-scaled spectrogram and a delta feature is calculated and added as an extra dimension for each segment. A Convolutional Neural Network is trained with processed audio files and manages to distinguish different boat types with an accuracy of 75%.

Abstract [sv]

En båt kan identifieras genom att analysera ljudet den skapar när den rör sig i vatten. Förmågan att identifiera båtar är viktig ur övervakningssynpunkt i den militära sektorn. Den här rapporten beskriver hur olika metoder inom ljudanalys och maskininlärning har implementerats, testats och utvärderats för att skapa en prototyp som kan identifiera olika båtar. Totalt 87 olika båtljud användes och behandlades på sju olika sätt.Inom området maskininlärning användes teknikerna ”Dense Neural Network”, ”Convolutional Neural Network” och ”Recurrent Neural Network” som tränades för att identifiera olika båttyper. Olika kombinationer av metoder inom ljudbehandling och maskininlärning testades med ett ”stratified Kfold” test för att utvärdera förmågan att klassificera olika båttyper.Resultatet blev en prototyp med en ljudbehandlingsmetod som delar upp en ljudfil i segment av samma storlek. Varje segment konverteras till ett ”logaritmiskt mel-scaled spectrogram” och en extra dimension med ett deltavärde adderas. Ett ” Convolutional Neural Network” tränas med de behandlade ljudfilerna och lyckas urskilja olika båtklasser med 75% sannolikhet.

Place, publisher, year, edition, pages
2019. , p. 72
Series
TRITA-EECS-EX ; 2019:262
Keywords [en]
Machine Learning, Neural Networks, Audio Recognition
Keywords [sv]
Maskininlärning, Neural Networks, Ljudigenkänning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254645OAI: oai:DiVA.org:kth-254645DiVA, id: diva2:1334329
Supervisors
Examiners
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-07-02Bibliographically approved

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CiteExportLink to record
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