Edge Impulse Experts / cough-monitor-audio (Created by Eivind Holt) Public

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

Audible events

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

60 ms

256 kB

1024 kB

Filters

Status

DSP type

Network type

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Data set

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General

F1-score

Precision

Recall

100%
spectr-conv1d-8a0
PERFORMANCE
LATENCY
99 ms of 60 ms
Exceeds target by 39 ms
RAM
24 kB of 256 kB
ROM
47 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.025 | 0.025 | -72

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

100%
mfcc-conv1d-d1d
PERFORMANCE
LATENCY
347 ms of 60 ms
Exceeds target by 287 ms
RAM
26 kB of 256 kB
ROM
44 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.025 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

99%
mfe-conv1d-88c
PERFORMANCE
LATENCY
169 ms of 60 ms
Exceeds target by 109 ms
RAM
24 kB of 256 kB
ROM
35 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.025 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
mfcc-conv1d-41c
PERFORMANCE
LATENCY
161 ms of 60 ms
Exceeds target by 101 ms
RAM
22 kB of 256 kB
ROM
70 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.05 | 32

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

98%
mfe-conv1d-a21
PERFORMANCE
LATENCY
179 ms of 60 ms
Exceeds target by 119 ms
RAM
24 kB of 256 kB
ROM
38 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.025 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
spectr-conv1d-bdb
PERFORMANCE
LATENCY
92 ms of 60 ms
Exceeds target by 32 ms
RAM
24 kB of 256 kB
ROM
47 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.025 | 0.025 | -32

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
mfcc-conv1d-085
PERFORMANCE
LATENCY
329 ms of 60 ms
Exceeds target by 269 ms
RAM
24 kB of 256 kB
ROM
34 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.025 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
mfe-conv1d-8d7
PERFORMANCE
LATENCY
102 ms of 60 ms
Exceeds target by 42 ms
RAM
20 kB of 256 kB
ROM
45 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.05 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
mfe-conv2d-6d8
PERFORMANCE
LATENCY
97 ms of 60 ms
Exceeds target by 37 ms
RAM
25 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.05 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

98%
spectr-conv1d-f6b
PERFORMANCE
LATENCY
211 ms of 60 ms
Exceeds target by 151 ms
RAM
38 kB of 256 kB
ROM
40 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.025 | 0.0125 | -72

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

97%
mfe-conv2d-998
PERFORMANCE
LATENCY
171 ms of 60 ms
Exceeds target by 111 ms
RAM
25 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFE

0.05 | 0.05 | 40

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

95%
spectr-conv2d-628
PERFORMANCE
LATENCY
415 ms of 60 ms
Exceeds target by 355 ms
RAM
21 kB of 256 kB
ROM
36 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

SPECTROGRAM

0.075 | 0.075 | -72

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

71%
mfcc-conv1d-ad2
PERFORMANCE
LATENCY
252 ms of 60 ms
Exceeds target by 192 ms
RAM
21 kB of 256 kB
ROM
34 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.025 | 32

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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

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

70%
mfcc-conv1d-1c1
PERFORMANCE
LATENCY
255 ms of 60 ms
Exceeds target by 195 ms
RAM
25 kB of 256 kB
ROM
45 kB of 1024 kB
DSP NN Unused
INPUT

1000 ms | 1000 ms

MFCC

0.05 | 0.025 | 32

ACCURACY
CLASSIFICATION (KERAS)

0.005 | 100

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