Feature extraction of surround sound recordings for acoustic scene classification

Abstract

This paper extends the traditional methodology of acoustic scene classification based on machine listening towards a new class of multichannel audio signals. It identifies a set of new features of five-channel surround recordings for classification of the two basic spatial audio scenes. Moreover, it compares the three artificial intelligence-based classification approaches to audio scene classification. The results indicate that the method based on the early fusion of features is superior compared to those involving the late fusion of signal metrics.