Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Discrete-time signal processing
Discrete-time signal processing
Fundamentals of speech recognition
Fundamentals of speech recognition
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Statistical methods for speech recognition
Statistical methods for speech recognition
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Analysis of chewing sounds for dietary monitoring
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Original papers: Acoustic monitoring system to quantify ingestive behavior of free-grazing cattle
Computers and Electronics in Agriculture
Automatic recognition of ingestive sounds of cattle based on hidden Markov models
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
A novel method to automatically measure the feed intake of broiler chickens by sound technology
Computers and Electronics in Agriculture
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In this work we propose a novel method to analyze and recognize automatically sound signals of chewing and biting. For the automatic segmentation and classification of acoustical ingestive behaviour of sheep the method use an appropriate acoustic representation and statistical modelling based on hidden Markov models. We analyzed 1813s of chewing data from four sheep eating two different forages typically found in grazing production systems, orchardgrass and alfalfa, each at two sward heights. Because identification of species consumed when in mixed swards is a key issue in grazing science, we tested the possibility to discriminate species and sward height by using the proposed approach. Signals were correctly classified by forage and sward height in 67% of the cases, whereas forage was correctly identified 84% of the time. The results showed an overall performance of 82% for the recognition of chewing events.