Open-set Evolving Acoustic Scene Classification System

Most audio recognition/classification systems assume a static and closed-set model, where training and testing data are drawn from a prior distribution. However, in real-world audio recognition/classification problems, such a distribution is unknown, and training data is limited and incomplete at training time. As it is difficult to collect exhaustive training samples to train classifiers. Datasets at prediction time are evolving and the trained model must deal with an infinite number of unseen/unknown categories. Therefore, it is desired to have an open-set classifier that not only accurately classifies the known classes into their respective classes but also effectively identifies unknown samples and learns them. This paper introduces an open-set evolving audio classification technique, which can effectively recognize and learn unknown classes continuously in an unsupervised manner. The proposed method consists of several steps: a) recognizing sound signals and associating them with known classes while also being able to identify the unknown classes; b) detecting the hidden unknown classes among the rejected sound samples; c) learning those novel detected classes and updating the classifier. The experimental results illustrate the effectiveness of the developed approach in detecting unknown sound classes compared to extreme value machine (EVM) and Weibull-calibrated SVM (W-SVM).