Edge Impulse Experts / cough-monitor-audio (Created by Eivind Holt) Public
Target: Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz) Clone this project
Experiments EON Tuner
The EON Tuner helps you find the most optimal architecture for your embedded machine-learning application. Clone this project to use the EON Tuner.

Target

No name set

Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz)

100 ms

256 kB

1024 kB

Filters

Status

DSP type

Model type

View

Data set

Variant

Sort

General

F1-score

Precision

Recall

98%
spectr-conv1d-8a0
Show logs
PERFORMANCE
LATENCY
99 ms of 100 ms
RAM
24 kB of 256 kB
ROM
47 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.025 | -72

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 98%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

5/24/2022, 7:30:12 PM

95%
spectr-conv1d-bdb
Show logs
PERFORMANCE
LATENCY
92 ms of 100 ms
RAM
24 kB of 256 kB
ROM
47 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.025 | -32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 95%

Type Filters Kernel Rate
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

5/24/2022, 7:30:29 PM

95%
spectr-conv1d-f6b
Show logs
PERFORMANCE
LATENCY
211 ms of 100 ms
Exceeds target by 111 ms
RAM
38 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.025 | 0.0125 | -72

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 95%

Type Filters Kernel Rate
Data augmentation
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.5

5/24/2022, 7:33:07 PM

94%
mfcc-conv1d-085
Show logs
PERFORMANCE
LATENCY
329 ms of 100 ms
Exceeds target by 229 ms
RAM
24 kB of 256 kB
ROM
34 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 94%

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.5

5/24/2022, 7:31:17 PM

94%
mfcc-conv1d-d1d
Show logs
PERFORMANCE
LATENCY
347 ms of 100 ms
Exceeds target by 247 ms
RAM
26 kB of 256 kB
ROM
44 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 94%

Type Filters Kernel Rate
Data augmentation
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

5/24/2022, 7:31:43 PM

94%
mfe-conv1d-8d7
Show logs
PERFORMANCE
LATENCY
102 ms of 100 ms
Exceeds target by 2 ms
RAM
20 kB of 256 kB
ROM
45 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 94%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.5

5/24/2022, 7:31:34 PM

94%
mfe-conv1d-88c
Show logs
PERFORMANCE
LATENCY
169 ms of 100 ms
Exceeds target by 69 ms
RAM
24 kB of 256 kB
ROM
35 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 94%

Type Filters Kernel Rate
Data augmentation
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.25

5/24/2022, 7:30:41 PM

93%
mfcc-conv1d-41c
Show logs
PERFORMANCE
LATENCY
161 ms of 100 ms
Exceeds target by 61 ms
RAM
22 kB of 256 kB
ROM
70 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.05 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 93%

Type Filters Kernel Rate
Data augmentation
conv1d 32 3 -
conv1d 64 3 -
conv1d 128 3 -
dropout - - 0.5

5/24/2022, 7:29:22 PM

93%
mfe-conv2d-998
Show logs
PERFORMANCE
LATENCY
171 ms of 100 ms
Exceeds target by 71 ms
RAM
25 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 93%

Type Filters Kernel Rate
Data augmentation
conv2d 8 3 -
conv2d 16 3 -
dropout - - 0.25

5/24/2022, 7:32:19 PM

92%
mfe-conv1d-a21
Show logs
PERFORMANCE
LATENCY
179 ms of 100 ms
Exceeds target by 79 ms
RAM
24 kB of 256 kB
ROM
38 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.025 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 92%

Type Filters Kernel Rate
Data augmentation
conv1d 16 3 -
conv1d 32 3 -
dropout - - 0.25

5/24/2022, 7:29:29 PM

92%
mfe-conv2d-6d8
Show logs
PERFORMANCE
LATENCY
97 ms of 100 ms
RAM
25 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 92%

Type Filters Kernel Rate
Data augmentation
conv2d 8 3 -
conv2d 16 3 -
dropout - - 0.5

5/24/2022, 7:33:57 PM

89%
spectr-conv2d-628
Show logs
PERFORMANCE
LATENCY
415 ms of 100 ms
Exceeds target by 315 ms
RAM
21 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

Type Filters Kernel Rate
Data augmentation
conv2d 8 3 -
conv2d 16 3 -
dropout - - 0.25

5/24/2022, 7:34:20 PM

70%
mfcc-conv1d-ad2
Show logs
PERFORMANCE
LATENCY
252 ms of 100 ms
Exceeds target by 152 ms
RAM
21 kB of 256 kB
ROM
34 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 70%

Type Filters Kernel Rate
conv1d 8 3 -
conv1d 16 3 -
dropout - - 0.25

5/24/2022, 7:29:10 PM

68%
mfcc-conv1d-1c1
Show logs
PERFORMANCE
LATENCY
255 ms of 100 ms
Exceeds target by 155 ms
RAM
25 kB of 256 kB
ROM
45 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.05 | 0.025 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 68%

Type Filters Kernel Rate
Data augmentation
conv1d 8 3 -
conv1d 16 3 -
conv1d 32 3 -
conv1d 64 3 -
dropout - - 0.25

5/24/2022, 7:29:48 PM

Help & documentation Get help on the forum Send feedback Contact sales
Join us
Terms of Service Privacy policy