In this paper, we describe a method for recognizing sound sources in a mixture. While many audio-based content analysis meth- ods focus on detecting or classifying target sounds in a discriminative manner, we approach this as a regression problem, in which we estimate the relative proportions of sound sources in the given mixture. Using certain source separation ideas, we directly estimate these proportions from the mixture without actually separating the sources. We also intro- duce a method for learning a transition matrix to temporally constrain the problem. We demonstrate the proposed method on a mixture of five classes of sounds and show that it is quite effective in correctly estimating the relative proportions of the sounds in the mixture