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In this paper, we approach the problem of audio summarization by saliency computation of audio streams, exploring the potential of a modulation model for the detection of perceptually important audio events based on saliency models, along with various fusion schemes for their combination. The fusion schemes include linear, adaptive and nonlinear methods. A machine learning approach, where training of the features is performed, was also applied for the purpose of comparison with the proposed technique. For the evaluation of the algorithm we use audio data taken from movies and we show that nonlinear fusion schemes perform best. The results are reported on the MovSum database, using objective evaluations (against ground-truth denoting the perceptually important audio events). Analysis of the selected audio segments is also performed against a labeled database in respect to audio categories, while a method for fine-tuning of the selected audio events is proposed.