This paper, presents an interactive approach for the analysis of electro-acoustic music. An original classification scheme is devised using relevance feedback and 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. An experimental study is conducted to evaluate and compare the different classification and relevance feedback approaches that are envisaged, using a database of poly-phonic pieces (with a varying degree of polyphony). The results show that the different approaches are adapted to different applications and they achieve satisfying performance in a reasonable number of iterations.