TimbralenAnalytic
TimbralenAnalytic is a framework for signal analysis that emphasizes timbre as a primary axis of description. It combines timbral feature extraction with linear analytic modeling to characterize complex signals such as music and environmental sounds. The approach aims to produce interpretable representations where components correspond to perceptually distinct timbral qualities, enabling comparisons across timbres, tracks, or source types while remaining applicable to non-audio time series.
Developed in the late 2010s by researchers in audio signal processing and computational linguistics, TimbralenAnalytic builds
Methodologically, TimbralenAnalytic proceeds through: (1) preprocessing and conditioning of the signal; (2) extraction of timbral descriptors
Applications include music information retrieval, sound synthesis, audio scene analysis, and human-computer interaction. Advantages include interpretability,