Acoustic context recognition using local binary pattern codebooks

Automatic context recognition enables mobile devices to adapt their configuration to different environments and situations. This paper investigates the use of acoustic cues as a means of recognising context. The majority of existing approaches exploit Mel-scaled cep-stral coefficients (MFCCs) developed for the analysis of speech signals. The hypothesis in this paper is that new features are needed in order to capture complex acoustic structure. The paper introduces the use of local binary pattern (LBP) analysis which is used to complement MFCCs with acoustic texture information. The second contribution relates to a bag-of-features extension which clusters LBPs into a small number of codewords. Both approaches outperform the current state of the art and the latter is particularly appealing for embedded applications in which computational efficiency is paramount.