A Sequential Algorithm for Signal Segmentation

• Published in: Entropy (Basel, Switzerland) Vol. 20; no. 1; p. 55
• Main Authors: Hubert, Paulo, Padovese, Linilson, Stern, Julio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2018; MDPI
• Subjects: Acoustic noise; Algorithms; audio segmentation; Bayesian analysis; bayesian methods; Economic models; hypothesis testing; signal detection
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e20010055

The problem of event detection in general noisy signals arises in many applications; usually, either a functional form of the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algorithm. There are situations, however, where neither functional forms nor annotated samples are available; then, it is necessary to apply other strategies to separate and characterize events. In this work, we analyze 15-min samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significant events. For that, we apply a sequential algorithm with the only assumption that an event alters the energy of the signal. The algorithm is entirely based on Bayesian methods.