  For music indexing robust to sound degradations and scalable for big music
catalogs, this scientific report presents an approach based on audio
descriptors relevant to the music content and invariant to sound
transformations (noise addition, distortion, lossy coding, pitch/time
transformations, or filtering e.g.). To achieve this task, one of the key point
of the proposed method is the definition of high-dimensional audio prints,
which are intrinsically (by design) robust to some sound degradations. The high
dimensionality of this first representation is then used to learn a linear
projection to a sub-space significantly smaller, which reduces again the
sensibility to sound degradations using a series of discriminant analyses.
Finally, anchoring the analysis times on local maxima of a selected onset
function, an approximative hashing is done to provide a better tolerance to bit
corruptions, and in the same time to make easier the scaling of the method.
