In addition to manually classifying calls, DeepSqueak includes two automated methods.

Unsupervised clustering applies featured-based machine learning with k-means to cluster calls, by minimizing the variance between a call's features and the nearest prototype cluster.

Supervised classification uses a convolution neural network to classify calls based on the spectrogram.

We've found that creating clustering with unsupervised methods, and using the cleanest clusters to train a supervised classification network, resulted in fast and accurate clustering.

Clusters may be viewed and renamed with "Tools > Call Classification > View Clusters"