title: Sound texture recognition through dynamical systems modeling of empirical mode decomposition.
abstract: This paper describes a system for modeling, recognizing, and classifying sound textures. The described system translates contemporary approaches from video texture analysis, creating a unique approach in the realm of audio and music. The signal is first represented as a set of mode functions by way of the Empirical Mode Decomposition technique for time/frequency analysis, before expressing the dynamics of these modes as a linear dynamical system (LDS). Both linear and nonlinear techniques are utilized in order to learn the system dynamics, which leads to a successful distinction between unique classes of textures. Five classes of sounds comprised a data set, consisting of crackling fire, typewriter action, rainstorms, carbonated beverages, and crowd applause, drawing on a variety of source recordings. Based on this data set the system achieved a classification accuracy of 90%, which outperformed both a Mel-Frequency Cepstral Coefficient based LDS-modeling approach from the literature, as well as one based on a standard Gaussian Mixture Model classifier.
authors.affiliation: School of Architecture and Electronic Arts Department, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, USA. vannod2@rpi.edu
authors.forename: Doug
authors.initials: D
authors.lastname: Van Nort
authors.forename: Jonas
authors.initials: J
authors.lastname: Braasch
authors.forename: Pauline
authors.initials: P
authors.lastname: Oliveros
mesh_terms: D016014:Linear Models; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography; D013997:Time Factors