Invariant Audio Prints for Music Indexing and Alignment

This work deals with music indexing and alignment using audio codes designed to be representative of the music content and robust to sound modifications. First, based on properties of the Fourier Transform and of the logarithm, high-dimensional audio descriptors are designed. Then, a dimension reduction is learned with criteria based on sound discrimination and invariance to transformations. Finally, a binarization is computed to derive codes (integers). This last process allows a fast searching for large catalogs with a hash table, and a Hamming distance on codes makes possible the time alignment using an adapted”Dynamic Time Warping”. The contributions of this paper are tested for two different tasks. The goal of the first task is to identify the segments of music medleys with the audio indexing process, and to accurately find the corresponding original time positions. The goal of the second task is to measure the accuracy of the time-alignment with synthesized MIDI files, where the tempo continuously varies, and with modified pitches and instruments. Additionally, the audio indexing is also tested for these data, in order to exhibit some properties of the used audio prints.