- feature : MFCC
signal_len < 100,000
... zero-paddingelse
... cut
- Pattern Learning Block(PLB)
- Proposed Model
- Number of parameters each modules
-
meaning
- proposed : proposed model
- others : remove module
- DWSCNN : Depth-wise seperable Convolution
- CBAM : Convolution Block Attention Module
- Spa : Spatial-attention
- SA : Self-attention
- routing : Dynamic Routing
-
EMO-DB
Name | #Params | max_WA(%) | min_WA(%) | avg_WA(%) | code |
---|---|---|---|---|---|
proposed | 95,288 | 90.76 | 85.11 | 88.18 | Link |
DWSCNN | 69,688 | 92.11 | 74.03 | 82.17 | Link |
CBAM | 90,994 | 94.49 | 78.79 | 85.88 | Link |
Spa | 93,770 | 92.11 | 82.38 | 87.24 | Link |
SA | 95,160 | 92.93 | 79.53 | 84.51 | Link |
routing | 70,712 | 87.42 | 70.83 | 78.66 | Link |
Δ | -3.73 | +2.73 | +0.94 |
- RAVDESS
Name | #Params | max_WA(%) | min_WA(%) | avg_WA(%) | code |
---|---|---|---|---|---|
proposed | 95,353 | 87.50 | 83.75 | 85.56 | Link |
DWSCNN | 69,753 | 78.12 | 65.62 | 72.68 | Link |
CBAM | 91,059 | 88.12 | 81.25 | 84.87 | Link |
Spa | 93,835 | 85.00 | 77.50 | 80.68 | Link |
SA | 95,225 | 82.50 | 74.37 | 78.50 | Link |
routing | 70,777 | 70.62 | 65.00 | 67.62 | Link |
Δ | -0.62 | +2.50 | +0.69 |
- IEMOCAP
Name | #Params | max_WA(%) | min_WA(%) | avg_WA(%) | code |
---|---|---|---|---|---|
proposed | 95,093 | 66.20 | 63.17 | 65.20 | Link |
DWSCNN | 69,493 | 65.77 | 59.52 | 62.72 | Link |
CBAM | 90,799 | 69.00 | 63.47 | 65.07 | Link |
Spa | 93,575 | 69.96 | 65.69 | 67.40 | Link |
SA | 94,965 | 67.18 | 60.91 | 64.56 | Link |
routing | 70,517 | 66.66 | 62.21 | 64.30 | Link |
Δ | -3.76 | -2.52 | -2.20 |
-
setting
batch_size = 1
Eq = all test dataset inference time / number of test dataset
- i.e. Average
-
Inference time / wav
(sec)
H/W | EMO-DB | RAVDESS | IEMOCAP |
---|---|---|---|
RTX 3080TI | 0.04371 | 0.03033 | 0.03416 |
i7-12700K | 0.05000 | 0.04545 | 0.04510 |
RTX 2080TI | 0.07182 | 0.06225 | 0.04953 |
i7-8700 | 0.07622 | 0.07257 | 0.06538 |
Raspberry Pi | 1.42443 | 1.35941 | 1.22835 |
- GPU peak memory usage
- Maximum usage of GPU memory at the moment
- via
tf.config.experimental.get_memory_info(‘GPU:0’)
- Model size
- saved model weights size
Model | Num.Params | Peak memory usage(GB) | Model size(Mb) |
---|---|---|---|
Proposed | 95K | 0.000627 | 0.433616 |
Non-commercial only