Bitrate classification of twice-encoded audio using objective quality features

Abstract

When a user uploads audio files to a music streaming service, these files are subsequently re-encoded to lower bitrates to target different devices, e.g. low bitrate for mobile. To save time and bandwidth uploading files, some users encode their original files using a lossy codec. The metadata for these files cannot always be trusted as users might have encoded their files more than once. Determining the lowest bitrate of the files allows the streaming service to skip the process of encoding the files to bitrates higher than that of the uploaded files, saving on processing and storage space. This paper presents a model that uses quality predictions from ViSQOLAudio, a full reference objective audio quality metric, as features in combination with a multi-class support vector machine classifier. An experiment on twice-encoded files found that low bitrate codecs could be classified using audio quality features. The experiment also provides insights into the implications of multiple transcodes from a quality perspective.