Datasets by Edge Impulse / Audio Classification - Keyword Spotting Public
Target: Cortex-M7 216MHz 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

Cortex-M7 216MHz

100 ms

340 kB

1024 kB

Filters

Status

DSP type

Model type

View

Data set

Variant

Sort

General

F1-score

Precision

Recall

94%
mfe-conv2d-879
Show logs
PERFORMANCE
LATENCY
190 ms of 100 ms
Exceeds target by 90 ms
RAM
42 kB of 340 kB
ROM
57 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.016 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 94%

Type Filters Kernel Rate
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.5

10/27/2022, 5:28:01 PM

93%
mfcc-conv2d-2ce
Show logs
PERFORMANCE
LATENCY
95 ms of 100 ms
RAM
16 kB of 340 kB
ROM
55 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 | 93%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.5

10/27/2022, 5:43:13 PM

91%
mfe-conv2d-30b
Show logs
PERFORMANCE
LATENCY
44 ms of 100 ms
RAM
36 kB of 340 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 91%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.5

10/27/2022, 5:19:47 PM

90%
mfe-conv2d-109
Show logs
PERFORMANCE
LATENCY
201 ms of 100 ms
Exceeds target by 101 ms
RAM
35 kB of 340 kB
ROM
44 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 90%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.5

10/27/2022, 5:51:05 PM

90%
mfe-conv2d-433
Show logs
PERFORMANCE
LATENCY
159 ms of 100 ms
Exceeds target by 59 ms
RAM
18 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 90%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:40:24 PM

89%
mfcc-conv2d-76c
Show logs
PERFORMANCE
LATENCY
312 ms of 100 ms
Exceeds target by 212 ms
RAM
35 kB of 340 kB
ROM
55 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 | 89%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:30:15 PM

89%
mfcc-conv2d-b02
Show logs
PERFORMANCE
LATENCY
113 ms of 100 ms
Exceeds target by 13 ms
RAM
20 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.01 | 40

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

Type Filters Kernel Rate
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.5

10/27/2022, 5:47:16 PM

89%
mfe-conv2d-885
Show logs
PERFORMANCE
LATENCY
217 ms of 100 ms
Exceeds target by 117 ms
RAM
31 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.05 | 0.05 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:35:36 PM

89%
mfcc-conv2d-a15
Show logs
PERFORMANCE
LATENCY
136 ms of 100 ms
Exceeds target by 36 ms
RAM
33 kB of 340 kB
ROM
55 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 | 89%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:42:23 PM

89%
mfcc-conv2d-a69
Show logs
PERFORMANCE
LATENCY
196 ms of 100 ms
Exceeds target by 96 ms
RAM
16 kB of 340 kB
ROM
55 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 | 89%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:15:32 PM

89%
mfe-conv2d-927
Show logs
PERFORMANCE
LATENCY
174 ms of 100 ms
Exceeds target by 74 ms
RAM
23 kB of 340 kB
ROM
40 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 89%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.25

10/27/2022, 5:48:32 PM

89%
mfcc-conv2d-e9e
Show logs
PERFORMANCE
LATENCY
192 ms of 100 ms
Exceeds target by 92 ms
RAM
36 kB of 340 kB
ROM
129 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 | 89%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
conv2d 128 3 -
dropout - - 0.5

10/27/2022, 5:46:10 PM

88%
mfcc-conv2d-7c9
Show logs
PERFORMANCE
LATENCY
677 ms of 100 ms
Exceeds target by 577 ms
RAM
32 kB of 340 kB
ROM
129 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.032 | 0.032 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 88%

Type Filters Kernel Rate
Data augmentation
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
conv2d 128 3 -
dropout - - 0.25

10/27/2022, 5:26:27 PM

87%
mfcc-conv2d-34e
Show logs
PERFORMANCE
LATENCY
485 ms of 100 ms
Exceeds target by 385 ms
RAM
45 kB of 340 kB
ROM
56 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFCC

0.02 | 0.01 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

Type Filters Kernel Rate
conv2d 8 3 -
conv2d 16 3 -
conv2d 32 3 -
conv2d 64 3 -
dropout - - 0.25

10/27/2022, 5:26:51 PM

87%
mfe-conv2d-156
Show logs
PERFORMANCE
LATENCY
103 ms of 100 ms
Exceeds target by 3 ms
RAM
49 kB of 340 kB
ROM
44 kB of 1024 kB
DSP NN Unused
TIME-SERIES INPUT

1000 ms |
1000 ms |
Enabled

MFE

0.02 | 0.02 | 32

ACCURACY (KERAS)
CLASSIFICATION (KERAS)

0.005 | 100 | 87%

Type Filters Kernel Rate
conv2d 16 3 -
conv2d 32 3 -
dropout - - 0.25

10/27/2022, 5:34:23 PM

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