Degradation-Invariant Music Indexing

Rémi Mignot (1) , Geoffroy Peeters (2, 3)

1 Analyse et synthèse sonores [Paris]
2 S2A - Signal, Statistique et Apprentissage
3 IDS - Département Images, Données, Signal

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.

Keywords: Audio indexing; Audio descriptors

Domains: Signal and Image Processing; Machine Learning [stat.ML]

Rémi Mignot, Geoffroy Peeters. Degradation-Invariant Music Indexing. STMS - Sciences et Technologies de la Musique et du Son UMR 9912 IRCAM-CNRS-Sorbonne Université. 2024. ⟨hal-04486105⟩