Four posts into this (one, two, three, four), it’s probably time to back up for a moment and recap.
Questions
I’ve been looking at two different questions:
Given raw MBARI hydrophone clips, can Perch 2.0:
- Distinguish between clips where a humpback vocalization is present vs not?
- Distinguish between different types of humpback vocalizations?
Data
Using three different groups of clips:1
- High-confidence detector-score clips (as estimated by the Google humpback detector model with scores of >=90% and <=5%)
- Lower-confidence detector-score clips (scores of 70–90% and 10–30%)
- A large (~9000) stratified sample across detector-score buckets
Assessment
And checking the results in two ways:
- 2D dimensionality reduction plots
- Manual listening to embedding nearest neighbors
Which ends up being twelve different potential experiments:
Present or Not
| PCA/UMAP | Nearest-neighbor listening | |
|---|---|---|
| High-confidence clips | Visually yes | later? |
| Lower-confidence clips | Not clearly | Not really |
| Stratified sample | later? | next? |
Types of vocalizations
| PCA/UMAP | Nearest-neighbor listening | |
|---|---|---|
| High-confidence clips | – | – |
| Lower-confidence clips | – | – |
| Stratified sample | later? | One tentative yes (bloops only so far) |
This is all still quite exploratory, but the matrix will keep me from treating different tests as if they answered the same question.
I don’t think I need to fill in all twelve boxes just for completeness, but the “next?” box looks like the best next step and I’ll keep the boxes marked “later?” in mind.
All clips are from the MBARI December 21, 2016 16 kHz full-day audio file. ↩︎