Following table shows SI-SDR improvements of the original paper and Asteroid's recipe. In average, our system is about 2dB better for the same number of parameters.
| task | Input (dB) | Paper | Here |
|---|---|---|---|
| sep_clean | 0.0 | 14.2 | 16.8 |
| sep_noisy | -4.5 | 12.0 | 13.7 |
| sep_reverb | -3.3 | 8.9 | 10.6 |
| sep_reverb_noisy | -6.1 | 9.2 | 11.0 |
All the experiments were done with the same configuration (only the tasks changes):
data:
mode: min
sample_rate: 8000
task: sep_clean
filterbank:
kernel_size: 40
n_filters: 512
stride: 20
masknet:
dropout: 0.3
n_layers: 4
n_src: 2
n_units: 600
optim:
lr: 0.001
optimizer: adam
weight_decay: 1.0e-05
training:
batch_size: 32
early_stop: true
epochs: 200
half_lr: trueThe following are output of final_metrics.json.
"si_sdr": 16.76808701529354,
"si_sdr_imp": 16.768125949735637,
"sdr": 17.189693158808677,
"sdr_imp": 17.037464558464684,
"sir": 27.281386306662466,
"sir_imp": 27.129157706318463,
"sar": 17.7691818584944,
"sar_imp": -131.3877807475557,
"stoi": 0.9642581421603139,
"stoi_imp": 0.22618525229628597"si_sdr": 9.25277109949865,
"si_sdr_imp": 13.739940125395641,
"sdr": 9.901496581627462,
"sdr_imp": 14.130608088232679,
"sir": 23.814840532053537,
"sir_imp": 23.6679900856008,
"sar": 10.323204569060126,
"sar_imp": 8.928649466834461,
"stoi": 0.8782651045376821,
"stoi_imp": 0.2505054514470455"si_sdr": 7.341333914145555,
"si_sdr_imp": 10.631356216662889,
"sdr": 9.431018910042448,
"sdr_imp": 9.547844736186036,
"sir": 21.850962651547228,
"sir_imp": 21.694702659905484,
"sar": 9.901812210974635,
"sar_imp": -10.838234734968795,
"stoi": 0.899942067670028,
"stoi_imp": 0.2242012600717384"si_sdr": 4.878757792806432,
"si_sdr_imp": 11.006090461420074,
"sdr": 6.692355035696564,
"sdr_imp": 10.178869336725988,
"sir": 20.067529432296666,
"sir_imp": 19.915254119873744,
"sar": 7.196904153627265,
"sar_imp": 4.430949537724975,
"stoi": 0.8279722173828477,
"stoi_imp": 0.22829729176536692