  When detecting anomalies in audio, it can often be necessary to consider
concept drift: the distribution of the data may drift over time because of
dynamically changing environments, and anomalies may become normal as time
elapses. We propose to use adaptive Huffman coding for anomaly detection in
audio with concept drift. Compared with the existing method of adaptive
Gaussian mixture modeling (AGMM), adaptive Huffman coding does not require a
priori information about the clusters and can adjust the number of clusters
dynamically depending on the amount of variation in the audio. To control the
size of the Huffman tree, we propose to merge clusters that are close to each
other instead of replacing rare clusters with new data. This reduces redundancy
in the Huffman tree while ensuring that it never forgets past information. On a
dataset of audio with concept drift which we have curated ourselves, our
proposed method achieves higher area under the curve (AUC) compared with AGMM
and fixed-length Huffman trees. The proposed approach is also time-efficient
and can be easily extended to other types of time series data (e.g., video).
