Context. Environmental Sound Recognition, unlike Speech Recognition, is an area that is still in the developing stages with respect to using Deep Learning methods. Sound can be converted into images by extracting spectrograms and the like. Object Recognition from images using deep Convolutional Neural Networks is a currently developing area holding high promise. The same technique has been studied and applied, but on image representations of sound. Objectives. In this study, investigation is done to determine the best possible accuracy of performing a sound classification task using existing deep Convolutional Neural Networks by comparing the data pre-processing parameters. Also, a novel method of combining different features into a single image is proposed and its effect tested. Lastly, the performance of an existing network that fuses Convolutional and Recurrent Neural architectures is tested on the selected datasets. Methods. In this, experiments were conducted to analyze the effects of data pre-processing parameters on the best possible accuracy with two CNNs. Also, experiment was also conducted to determine whether the proposed method of feature combination is beneficial or not. Finally, an experiment to test the performance of a combined network was conducted. Results. GoogLeNet had the highest classification accuracy of 73% on 50-class dataset and 90-93% on 10-class datasets. The sampling rate and frame length values of the respective datasets which contributed to the high scores are 16kHz, 40ms and 8kHz, 50ms respectively. The proposed combination of features does not improve the classification accuracy. The fused CRNN network could not achieve high accuracy on the selected datasets. Conclusions. It is concluded that deep networks designed for object recognition can be successfully used to classify environmental sounds and the pre-processing parameters’ values determined for achieving best accuracy. The novel method of feature combination does not significantly improve the accuracy when compared to spectrograms alone. The fused network which learns the special and temporal features from spectral images performs poorly in the classification task when compared to the convolutional network alone.

Artificial neural networks have in the last decade been a vital tool in image recognition, signal processing and speech recognition. Because of these networks' ability to be highly flexible, they suit a vast amount of different data. This flexible attribute is very sought for within the field of environmental sound classification. This thesis seeks to investigate if audio from three types of water usage can be distinguished and classified. The usage types investigated are handwashing, showering and WC-flushing. The data originally consisted of sound recordings in WAV format. The recordings were converted into spectrograms, which are visual representations of audio signals. Two neural networks are addressed for this image classification issue, namely a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN). Further, these spectrograms are subject to both image preprocessing using a Sobel filter, a Canny edge detector and a Gabor filter while also being subjected to data augmentation by applying different brightness and zooming alterations. The result showed that the CNN gave superior results compared to the MLP. The image preprocessing techniques did not improve the data and the model performances, neither did augmentation or a combination between them. An important finding was that constructing the convolutional and pooling filters of the CNN into rectangular shapes and using every other filter type horizontally and vertically on the input spectrogram gave superior results. It seemed to capture more information of the spectrograms since spectrograms mainly contain information in a horizontal or vertical direction. This model achieved 91.14% accuracy. The result stemming from this model architecture  further contributes to the environmental sound classification community. / <p>Masters thesis approved 20th june 2022.</p>