Shankar-16 / CLASSIFICATION OF DIFFERENT SPECIES Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name	Expected outcome	Length	Result
Recording (5) (mp3cut.net) (2).wav.24bbkld4	Recording	1m 27s	143 Sista Voice, 103 My Voice, 78 uncertain, 10 Dog, 6 Birds, 3 Cat	Show classification Show in data acquisition Show in feature explorer
Recording (5) (mp3cut.net) (1).wav.24bbke0t	Recording	43s	85 My Voice, 46 Sista Voice, 33 uncertain, 4 Dog, 1 Birds, 1 Cat	Show classification Show in data acquisition Show in feature explorer
Recording (5) (mp3cut.net).wav.24bbivh7	Recording	43s	166 My Voice, 3 uncertain, 1 Sista Voice	Show classification Show in data acquisition Show in feature explorer
cat-3338.wav.24aqhbfk	cat-3338	21s	56 Dog, 19 Birds, 4 uncertain, 1 Cat, 1 Sista Voice	Show classification Show in data acquisition Show in feature explorer
Dog_Barking2.wav.24aqdv4f	Dog_Barking2	50s	93 uncertain, 51 Birds, 48 Sista Voice, 3 Dog, 2 Cat	Show classification Show in data acquisition Show in feature explorer
Birds_Chirping1.wav.231n5kth	Birds_Chirping1	1m 25s	335 Birds, 2 uncertain	Show classification Show in data acquisition Show in feature explorer
WhatsApp Audio 2021-04-12 at 5.44.36 PM.wav.231l8f0n	WhatsApp Audio 2021-04-12 at 5	2m 7s	247 Sista Voice, 168 uncertain, 52 Dog, 25 Birds, 10 My Voice, 1 Cat	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

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Confusion matrix

Feature explorer

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

NN Classifier

Minimum confidence rating

Should be numeric

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	S Shankar-16

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Test data

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Model testing output

Results

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Confusion matrix

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