Download Analysing auditory representations for sound classification with self-organising neural networks Three different auditory representations—Lyon’s cochlear model, Patterson’s gammatone filterbank combined with Meddis’ inner hair cell model, and mel-frequency cepstral coefficients—are analyzed in connection with self-organizing maps to evaluate their suitability for a perceptually justified classification of sounds. The self-organizing maps are trained with a uniform set of test sounds preprocessed by the auditory representations. The structure of the resulting feature maps and the trajectories of the individual sounds are visualized and compared to one another. While MFCC proved to be a very efficient representation, the gammatone model produced the most convincing results.
Download Soundspotter - A Prototype System for Content-based Audio Retrieval We present the audio retrieval system “Soundspotter,” which allows the user to select a specific passage within an audio file and retrieve perceptually similar passages. The system extracts framebased features from the sound signal and performs pattern matching on the resulting sequences of feature vectors. Finally, an adjustable number of best matches is returned, ranked by their similarity to the reference passage. Soundspotter comprises several alternative retrieval algorithms, including dynamic time warping and trajectory matching based on a self-organizing map. We explain the algorithms and report initial results of a comparative evaluation.