In this paper, we present an interactive approach for the classification of sound objects in electro-acoustic music. For this purpose, we use relevance feedback combined with active-learning segment selection in an interactive loop. Validation and correction information given by the user is injected in the learning process at each iteration to achieve more accurate classification. Three active learning criteria are compared in the evaluation of a system classifying polyphonic pieces (with a varying degree of polyphony). The results show that the interactive approach achieves satisfying performance in a reasonable number of iterations.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.