Introducing the ReaLISED Dataset for Sound Event Classification

This paper presents the Real-Life Indoor Sound Event Dataset (ReaLISED), a new database which has been developed to contribute to the scientific advance by providing a large amount of real labeled indoor audio event recordings. They offer the scientific community the possibility of testing Sound Event Classification (SEC) algorithms. The full set is made up of 2479 sound clips of 18 different events, which were recorded following a precise recording process described along the proposal. This, together with a described way of testing the similarity of new audio, makes the dataset scalable and opens up the door to its future growth, if desired by the researchers. The full set presents a good balance in terms of the number of recordings of each type of event, which is a desirable characteristic of any dataset. Conversely, the main limitation of the provided data is that all the audio is recorded in indoor environments, which was the aim behind this development. To test the quality of the dataset, both the intraclass and the interclass similarities were evaluated. The first has been studied through the calculation of the intraclass Pearson correlation coefficient and further discard of redundant audio, while the second one has been evaluated with the creation, training and testing of different classifiers: linear and quadratic discriminants, k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Deep Neural Networks (DNN). Firstly, experiments were carried out over the entire dataset, and later over three different groups (impulsive sounds, non-impulsive sounds, and appliances) composed of six classes according to the results from the entire dataset. This clustering shows the usefulness of following a two-step classification process