The codes here are implementations of attention-based scaling adaptation (ASA), which introduces a special attention mechanism to investigate the dynamic interaction between different mixtures and the target speaker to exploit the discriminative target speaker clues. Under the same network configurations, the ASA in a single-channel condition can achieve competitive performance gains as that achieved from two-channel mixtures with inter-microphone phase difference (IPD) features.
To optimize the memory during training process, the memonger technology is applied.
- Mixtures,
cd ./data/anechoic
, modify corresponding data path and runsh run_data_generation.sh
. - Reverberation,
cd ./data/reverberate
, modify corresponding data path and runsh launch_spatialize.sh
.
- Training: configure the conf.py and run
sh train.sh 0 asa
. - Evaluate: modify the corresponding data of decode.sh and run
sh decode.sh asa
.
System | FF | MM | FM | Avg |
---|---|---|---|---|
(0) TSB | 9.43 / 8.84 | 10.02 / 9.52 | 12.54 / 12.06 | 11.26 / 10.76 |
(1) TSB (IPD) | 10.01 / 9.46 | 10.51 / 10.02 | 12.80 / 12.31 | 11.65 / 11.15 |
(2) ASA | 9.78 / 9.23 | 10.36 / 9.86 | 12.78 / 12.29 | 11.55 / 11.05 |
(3) ASA (MP) | 9.83 / 9.26 | 10.47 / 9.97 | 12.89 / 12.40 | 11.65 / 11.15 |
Email: [email protected]
- Code & Scripts
- Paper