Music indexing allows the finding of music excerpts among a large music catalog and the detection of duplicates. With the rise of social media, it is more and more important for music owners to detect misuse and illegal use of their music. The main difficulty of this task is the detection of music excerpts when they have been strongly modified, intentionnally or not. To deal with this issue, the presented method is based on an audio representation relevant to the music content and robust to some sound modifications. Then, using a data augmentation approach, a discriminant transformation is learnt to improve the robustness of the compact representation. Finally, a hash function is derived to allow a fast searching with a large catalog together with a robustness to bit corruption.