  In this work, we propose a framework to apply Huffman coding for anomaly
detection in audio. There are a number of advantages of using the Huffman
coding technique for anomaly detection, such as less dependence on the a-priory
information about clusters (e.g., number, size, density) and variable event
length. The coding cost can be calculated for any duration of the audio.
Huffman codes are mostly used to compress non-time series data or data without
concept drift. However, the normal class distribution of audio data varies
greatly with time due to environmental noise. In this work, we explore how to
adapt the Huffman tree to incorporate this concept drift. We found that,
instead of creating new nodes, merging existing nodes gives a more effective
performance. Note that with node merging, you never actually forget the
history, at least theoretically. To the best of our knowledge, this is the
first work on applying Huffman coding techniques for anomaly detection in
temporal data. Experiments show that this scheme improves the result without
much computational overhead. The approach is time-efficient and can be easily
extended to other types of time series data (e.g., video).
