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There is a huge amount of audio data available that is compressed using the MPEG audio compression standard. Sound analysis is based on the computation of short time feature vectors that describe the instantaneous spectral content of the sound. An interesting possibility is the calculation of features directly from compressed data. Since the bulk of the feature calculation is performed during the encoding stage this process has a significant performance advantage if the available data is compressed. Combining decoding and analysis in one stage is also very important for audio streaming applications. In this paper, we describe the calculation of features directly from MPEG audio compressed data. Two of the basic processes of analyzing sound are: segmentation and classification. To illustrate the effectiveness of the calculated features we have implemented two case studies: a general audio segmentation algorithm and a music/speech classifier. Experimental data is provided to show that the results obtained are comparable with sound analysis algorithms working directly with audio samples.