Slow and steady: auditory features for discriminating animal vocalizations
DiTullio, R. W.; Wei, L.; Balasubramanian, V.
Show abstract
We propose that listeners can use temporal regularities - spectro-temporal correlations that change smoothly over time - to discriminate animal vocalizations within and between species. To test this idea, we used Slow Feature Analysis (SFA) to find the most temporally regular components of vocalizations from birds (blue jay, house finch, American yellow warbler, and great blue heron), humans (English speakers), and rhesus macaques. We projected vocalizations into the learned feature space and tested intra-class (same speaker/species) and inter-class (different speakers/species) auditory discrimination by a trained classifier. We found that: 1) Vocalization discrimination was excellent (> 95%) in all cases; 2) Performance depended primarily on the [~]10 most temporally regular features; 3) Most vocalizations are dominated by [~]10 features with high temporal regularity; and 4) These regular features are highly correlated with the most predictable components of animal sounds.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong 96%
- Analysis of Ultrasonic Vocalizations from Mice Using Computer Vision and Machine Learning 95%
- A statistical framework to assess cross-frequency coupling while accounting for modeled confounding effects 95%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.