Résumé:
Acoustic Scene Classification (ASC) is the task to identify audio recordings into one of
several predefined acoustic scene classes. There is a huge amount of systems that have been
developed to tackle the ASC problem using different machine learning-based techniques.
Therefore, building a program that combines multiple techniques has become a necessary need
to properly assess the effects of these techniques on the performance of the ASC systems and
compare between it in an easy way. Motivated by these needs, we have designed in our work
an acoustic scene classification-based workbench that consists of variety of techniques and
methods related to this task. The goal of this work is to allow the users to apply the techniques
they are desiring to use on a chosen dataset, make changes in the parameters fast, create big
tests, and visualize the results of these tests. To accomplish this task, we have created a
workbench that is composed of modules consisting various tools for each module. Specifically,
we have implemented 5 modules concerning the ASC task stages which are respectively: Data
loading and visualization, for importing a Dataset and get an overview of it. Data
transformation, for extracting relevant features from the dataset. Data augmentation, for
increasing data size. Training, for train the employed Deep Neural Networks on the obtained
dataset and evaluate its performance. And lastly Prediction, for evaluating the performance of
the trained models on making an accurate class prediction of a chosen audio recording.
Thereafter, for executing our workbench to see how it performs, we have used a modified TAU
Urban Acoustic Scenes 2019 dataset that consist of 7192 audio recordings with 10 various
classes on 5 different cities for each, and tested it on different existing techniques while
recording the obtained accuracy on a table of results.
Keywords: Acoustic scene classification, Workbench, Machine learning, Data Augmentation,
Deep Neural Network.
