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The 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 analysis 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.