Sooriyaarachchi SEgodage D202120212021Egodage, D. (2021). Automatic classification of multiple acoustic events using artificial neural networks [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22276http://dl.lib.uom.lk/handle/123/22276There are numerous scenarios where similar acoustic events occur multiple times. Acoustic monitoring of migratory birds is an ideal example. Birds make a type of call known as flight calls during migration. A flight call can be considered as an acoustic event because it is a short-term, intuitively distinct sound. It is challenging to identify multiple occurrences of extremely short-range acoustic events such as flight calls in real-world recordings using classification techniques that require more computational power. It is mainly due to background noise and complex acoustic environments. This research aims at developing a classification model that reduces the effect of background noise, extract ROIs from continuous recordings, extract suitable features of flight calls and detect multiple occurrences of flight calls. An improved algorithm that can extract features has been developed in this research—by combining a well known Maximally Stable Extremal Regions (MSER) technique with state of the art traditional techniques. Namely Spectral and Temporal Features(SATF) and a combination of SATF and Spectrogram-based Image Frequency Statistics(SIFS). We name this novel algorithm as Spectrogram-based Maximally Stable Extremal Regions (SMSER). Three distinct feature sets have formed such that Featureset-1 created using SATF. Featureset-2 is a blend of SATF and SIFS. Featureset-3 is a combination of SATF, SIFS, and SMSER. The kNN, RF, SVM, and DNN classification techniques evaluated a real-world dataset using the extracted feature sets. Research carried out several tests to find out the best performing combination of classification model and feature set. The results showed that the flight calls’ detection accuracy increased when the number of features increased, although high computational power requirement is a disadvantage. The performance of SMSER feature set was the best among almost every classification technique above. It should be because the SMSER Feature set has the highest number of features. Classification of the SMSER feature set from the DNN classifier showed the highest accuracy of 87.67%.enFEATURE EXTRACTIONAUTOMATIC CLASSIFICATIONARTIFICIAL NEURAL NETWORKSCOMPUTER SCIENCE- DissertationCOMPUTER SCIENCE & ENGINEERING - DissertationThesis-AbstractEngineeringMSc in Computer Science & Engineering By researchDepartment of Computer Science & EngineeringTH4863