Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Semantic context detection based on hierarchical audio models
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Generative and Discriminative Modeling toward Semantic Context Detection in Audio Tracks
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Proposition of common classifier construction for pattern recognition with context task
Knowledge-Based Systems
Semantic context detection using audio event fusion: camera-ready version
EURASIP Journal on Applied Signal Processing
Real-world acoustic event detection
Pattern Recognition Letters
Sound event recognition through expectancy-based evaluation ofsignal-driven hypotheses
Pattern Recognition Letters
Learning to be energy-wise: discriminative methods for load disaggregation
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
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Two sound classifiers were proposed for a novel aquaculture application that involved processing sound to estimate the feed consumption of prawns within the turbid waters of farm ponds. A two stage content classifier inferred feed events using identified sound features. To deal with the class ambiguity created by the acoustically challenging conditions of ponds, the CADBN was proposed to jointly model the sound features with the context of feed events. The CADBN was then reformulated to classify the energy load of devices using a distributed state space that enabled flexible and efficient modelling of context. The CADBN was compared to a set of benchmark classifiers for both the prawn feeding and energy applications. Results indicate that the inclusion of context greatly enhances class discrimination in both problems. Furthermore, results illustrate that the temporal structure of the CADBN produced superior performance to benchmark context classifiers that adopt the same context features as independent inputs.