Sparse Coding for Efficient Bioacoustic Data Mining: Preliminary Application to Analysis of Whale Songs
Bibliographic record
Abstract
Bioacoustic monitoring, such as surveys of animal populations and migration, needs efficient data mining methods to extract information from large datasets covering multi-year and multi-location recordings. This paper introduces a method for sparsecoding of bioacoustic recordings in order to efficiently compress and automatically extract patterns in data. We demonstrate the proposed method on the analysis of humpback whale songs. Previous work suggests that the structure of these songs can be characterized by successive vocalizations called sound units. Most of these analyses are currently done with expert intervention, but the volume of recordings drive the need for automated methods for sound unit classification. This paper proposes that sparse coding of the song at different time scales supports the distinction of stable song components versus those which evolve year to year. The approach is summarized as: first, an unsupervised method is used to encode the entire bioacoustic dataset into a dictionary, second, sparse coding is used to limit the number of elements in the dictionary, third, salientfeatures are identified using the Lasso algorithm, and finally, an interpretation of the evolving and stable components of the songs is derived, supporting an analysis of year to year variation. It is shown that shorter codes are more stable, occurring with similar frequency across two consecutive years, while the occurrence of longer units varies across years as expected based on the prior manual analysis. 250 ms segments appear to be an appropriate length for encoding stable features of whale songs, possibly corresponding to subunits. We conclude by exploring further possibilities of the application of this method for biopopulation analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".