skraak.kiwi

2022-12-17

To: Pomona

How: By bicycle, foot, packraft, more packraft, boat thanks to Pomona Trust, car, bicycle.

Overview

LocationSolo_MaleSolo_FemaleDuetsIndividualFalse_Positives
F09583669183141
C05530524968042
H04255544840554
N20214204632634
M04132634127741
D03138205025824
N1494345123059
D09155321120967
WD051771118053
T10107201515719
F051492215557
K09402698411
S13T483218215
KS06321511698
E075504634
J1125233311
A1171082
TOTAL27414404334047542

Calls per Hour

locationmalefemaleduetindividual
F091.22550.28550.16551.511
N201.09240.27730.19331.3697
C051.05270.18360.08911.2363
N140.54920.3220.19320.8712
H040.55090.18550.08730.7364
T100.50620.14520.06220.6514
M040.31450.18910.07450.5036
D030.3170.1180.08430.435
D090.30130.0780.020.3793
K090.20250.14460.03720.3471
WD050.32360.00360.00180.3272
S13T0.17820.120.00360.2982
F050.27960.00740.00370.287
KS060.15030.09090.03850.2412
E070.10730.00730.00730.1146
J110.05090.00910.00550.06
A110.02860.00410.00.0327
TOTAL0.42530.12770.06280.5531

Changes

Female Calls per Hour


Male Calls per Hour

Newsletter

2022-12-29

I finished the first pass over the data from my last trip on the 17th of December.

Prior to the trip I had trained my own binaray classifier on 160gb of mostly Pomona data. About 10% was from Secretary Island, in total 4% of the audio was actual kiwi calls. It had very good validation statistics on the 20% data held out from training to check results after each epoch. Opensoundscape/pytorch made very good use of compute once I put the data on the ssd instead of the hard drive. It completed an epoch, including validation every hour, using 12 cores and big chunks of 100% GPU. It trained for 95 epoch’s, but I lost the best model due to a mistake I made, and ended up using the best model from around 75 epochs, there was very little difference in their statistics, decimal places.

Predicting with the model was extremely impressive, it finished processing 800gb raw audio in hours, AviaNZ would have taken a week running 24/7. I ran it over all my data, close to 6TB in a few days, less time than I would have taken processing 1 trip worth of audio using AviaNZ. The results are unprocessed except for the new data fetched in December. Time is short.

I over ran my usage limits on Airtable which I was using for the first pass over detections. I found a very efficient workflow on my MacBook. I merge the spectrogram image and audio file into a video, all the details I need are in the file name, and I use tags in Finder to label files. The tag is written into the file metadata and I can later retrieve a long list of labels for each file. Working with Finder is extremely efficient, you can tag files with keyboard shortcuts, even tag multiple files at the same time, sort tags, etc. Finder is brilliant. The labels stick to the file because they are part of it.

When predicting, Opensoundscapes looks at 5s chunks of audio with a 2.5s overlap. I get a long list of segmeents for each file, with a 0 or 1 assigned. There is some stuff going on there, to get a binary result and I may need to do some refining but overall it is very good. I then take that list, exclude any detection that falls in the day, defined by civil twilight. I made a thing that chunks it all up into actual calls, essentially I discard any detection that has no other detections nearby, but anything within 10s of any other detections gets chunked up. It works extremely well, no more half calls, except when they overflow a file during recording.

I have simplified my labels, for kiwi, all I now label are Male/Female, and I mark Close calls so I can find them. I Plan to find duets algorithmically in future.

I had a mind boggling 10 716 detections to wade through:

This model detects a lot more calls than I am used to. Previously I had detected a total of around 5400 calls on Pomona. Must have been missing many many calls. I will find them.

First I need to reduce my false positives.

The Plan:

I am aiming to label calls automatically in future, just weed out a few exceptions. Or that is wnat my goal is anyway.

In future my skraak notbook will need to be re-written to accomodate a simpler, more automated labelling scheme.

Video

Audio

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Thanks to the Pomona Island Charitable Trust
Supported by Fiordland Packs

CC BY-SA 4.0 David Cary. Last modified: July 29, 2023.