Spatial Audio Scene Characterization (SASC) - Automatic Classification of Five-Channel Surround Sound Recordings According to the Foreground and Background Content (original) (raw)

2018

Abstract

Spatial audio becomes increasingly popular in domestic and mobile multimedia applications. Evaluating quality of experience (QoE) of such applications requires the development of algorithms capable of identification and quantification of perceptual characteristics of spatial audio scenes. This paper introduces a method for the automatic categorization of surround sound recordings using a criterion based on the distribution of foreground and background audio content around a listener. The principles of the method were demonstrated using a study in which a corpus of 110 five-channel surround sound recordings was computationally classified according to the two basic spatial audio scene categories. In order to develop the proposed method a novel metric, representing spatial audio characteristics, was identified. Moreover, five machine learning algorithms, including neural networks, random forests and support vector machines, were employed and their performance compared. According to the...

Sławomir Zieliński hasn't uploaded this paper.

Let Sławomir know you want this paper to be uploaded.

Ask for this paper to be uploaded.