Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio
This work addresses the challenging task of detecting specific notes in audio recordings with outliers, with applications in bioacoustics for animal sound detection and musicology, but it is incremental as it adapts an existing HLDS architecture for a specific purpose.
The paper tackled the problem of simultaneous segmentation and classification of notes from audio recordings in the presence of outliers, using a hierarchical linear dynamical system (HLDS) with a novel parameter setting method, achieving automated classification of notes of interest in test clips containing outliers.
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.