Audio scene analysis using parametric signal features

The objective of this paper is to propose pole modeling-based features for audio classification in order to achieve a high classification performance. This paper investigates the suit- able pole modeling computation method and evaluates the proposed audio features in an audio database with 40 human speech samples, and 40 non human audio signals including aircraft, helicopter, drum, flutes, and piano sounds. An accuracy rate of 85% is acheived using the pole modeling features and linear discriminant analysis (LDA). We also compare the performance of the pole modeling features with two well-known audio features: Autoregressive (AR), and Mel-frequency Cepstral coefficients (MFCCs). We found that pole modeling is an appropriate tool for real-time audio scene analysis.