Table 1 Description of the training, standardized and experimental data sets used in this study. The table represents the number of calls for each call type included in the respective data set. Ncalls number of calls, NInd/groups number of individuals or groups from which the calls were emitted.

From: Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations

Data set

Trill

Long whistle

Short whistle

Tsak

Zip

Ncalls

NInd/groups

Ncalls

NInd/groups

Ncalls

NInd/groups

Ncalls

NInd/groups

Ncalls

NInd/groups

Training data set

Detection (2123 calls)

326

12

159

4

1109

6

519

5

10

1a

Classification and clustering (2257 calls)

302

52

186

24

1158

46

541

34

70

20

Standardized data set

Good-quality (450 calls)

50

42

50

22

150

40

150

29

50

19

Clipped (80 calls)

10

9

10

8

30

9

30

10

Low-amplitude (80 calls)

10

7

10

10

30

10

30

10

Overlaid (80 calls)

10

8

10

8

30

8

30

9

Original experimental data set

M. murinus (3040 calls)

76

7

157

5

2582

8

195

6

30

7

M. lehilahytsara (1115 calls)

48

5

526

5

240

1

193

3

108

4

  1. aZips occur rarely in a sufficient signal-to-noise ratio. However, because they are highly stereotyped, this low number of Zips turned out to be sufficient to train the detector.