A new technique for sound-scene analysis is presented. This technique operates by discovering common modulation behavior among groups of frequency subbands in the autocorrelogram domain. The analysis is conducted by first analyzing the autocorrelogram to estimate the amplitude modulation and period modulation of each channel of data at each time step, and then using dynamic clustering techniques to group together channels with similar modulation behavior. Implementation details of the analysis technique are presented, and its performance is demonstrated on a test sound.