Current methods for the accurate recognition of instruments within music are based on discriminative data descriptors. These are features of the music fragment that capture the characteristics of the audio and suppress details that are redundant for the problem at hand. The extraction of such features from an audio signal requires the user to set certain parameters. We propose a method for optimizing the parameters for a particular task on the basis of the Simulated Annealing algorithm and Support Vector Machine classification. We show that using an optimized set of audio features improves the recognition accuracy of drum sounds in music fragments
