Outlier Learning via Augmented Frozen Dictionaries
Brandon T. Carroll, Bradley M. Whitaker, Wayne Dayley, David V. Anderson · IEEE/ACM Transactions on Audio Speech and Language Processing · 2017
A frozen dictionary learning method is proposed to automatically separate the aspects of environmental audio that are different between normal data and anomalous data. The approach involves learning a dictionary-based sparse representation of the normal data, then freezing this portion of the dictionary while an added portion of the dictionary is learned on the anomalous data. Two dictionary learning algorithms are modified to allow training some elements while holding others constant. Both algorithms demonstrate the ability to separate the anomalies for two sets of test data. One set is chicken recordings with human crowd noise anomalies artificially added at -6 dB. The other set consists of recordings of chickens that are healthy and sick. Both dictionary methods are able to identify data anomalies with high accuracy and precision.