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We seek to develop simultaneous segmentation and classification of notes fromaudio recordings in presence of outliers. The selected architecture formodeling time series is hierarchical linear dynamical system (HLDS). We proposea novel method for its parameter setting. HLDS can potentially be employed intwo ways: 1) simultaneous segmentation and clustering for exploring data, i.e.finding unknown notes, 2) simultaneous segmentation and classification of audiorecording for finding the notes of interest in the presence of outliers. Weadapted HLDS for the second purpose since it is an easier task and still achallenging problem, e.g. in the field of bioacoustics. Each test clip has thesame notes (but different instances) as of the training clip and also containoutlier notes. At test, it is automatically decided to which class of interesta note belongs to if any. Two applications of this work are to the fields ofbioacoustics for detection of animal sounds in audio field recordings and alsoto musicology. Experiments have been conducted for segmentation andclassification of both avian and musical notes from recorded audio.

Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio