Enhanced Streaming Based Subspace Clustering Applied to Acoustic Scene Data Clustering

Abstract

Labelled data are often required to train an acoustic scene classification system. However, it is time-consuming and expensive to label the data manually. An unsupervised clustering algorithm can be used to facilitate the labelling process by dividing the acoustic data into different categories. Nevertheless, it can be problematic to run a clustering algorithm with growing data volume and dimension due to the sharp increase in the computational and memory costs. We propose a new streaming based subspace clustering algorithm which allows the data to be clustered on the fly, and also resolves data points in the overlapping regions of two subspaces by augmenting the learned low-rank representation with the original data samples. Experimental results show that our method can achieve the clustering objective for overwhelmingly high-volume data in an online fashion, while retaining good accuracy and reducing the memory cost significantly.