To improve the reliability and the scalability of content-based retrieval of variant audio tracks from large music databases, we suggest a new multi-stage LSH scheme consisting in (i) the extraction of compact but accurate representations from audio tracks by exploiting the LSH idea to summarize audio tracks, and (ii) an adequate organization of the resulting representations in LSH tables, retaining almost the same accuracy as an exact kNN retrieval. In the first stage we use major bins of successive chroma features and calculate a multi-probe histogram (MPH) that is concise but retains the information about local temporal correlations. In the second stage, based on the order statistics (OS) of MPH, we propose a new LSH scheme, OS-LSH, to organize and probe the histograms. The representation and organization of the audio tracks are storage efficient and support robust and scalable retrieval. Extensive experiments over a large dataset with 30,000 real audio tracks confirm the effectiveness and efficiency of the proposed scheme.