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