A granular approach for the analysis of monophonic audio signals
Résumé
This paper describes a method for analyzing audio signals with an adaptive ``parametric dictionary''. We use sliding frames to extract elementary signals or grains from the anal- ysis signal. We search for similarities amongst the collected grains to form classes, which we then use to derive a signal model for each class. These signal models or prototypes, are used to decompose the audio signal and compute analysis parameters for each grain. As a preliminary evaluation, we tested the method with real-life, monophonic and monaural recordings and obtained encouraging results.
Origine | Accord explicite pour ce dépôt |
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