SAR-SSL

A python implementation of “Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer”

Contributions
- Self-supervised learning of spatial acoustic representation (SSL-SAR)
  - first self-supervised learning method in spatial acoustic representation learning and multi-channel audio signal processing
  - designs cross-channel signal reconstruction pretext task to learn the spatial acoustic and the spectral pattern information
  - learns useful knowledge that can be transferred to the spatial acoustics-related tasks
- Multi-channel audio Conformer (MC-Conformer)
  - unified architecture for both the pretext and downstream tasks
  - learns the local and global properties of spatial acoustics present in the time-frequency domain
  - boosts the performance of both pretext and downstream tasks

Datasets

Source signals: from WSJ0 database
Simulated RIRs: generated by gpuRIR toolbox
Simulated noise: generated by arbitrary noise field generator

Real-world RIRs or microphone signals: from MIR, MeshRIR, DCASE, dEchorate, BUTReverb, ACE, LOCATA databases

Datasets	#Room	Microphone Array	#Mic. Pair	#Room x #Source position x #Array position	Noise Type
MIR	3	Three 8-channel linear arrays	60	3 x 26 x 1	W/o
MeshRIR	1	441 microphones	8874	1 x 32 x 1	W/o
DCASE	9	A 4-channel tetrahedral array (EM32)	3	38530	Ambience
dEchorate	11	Six 5-channel linear arrays	48	11 x 3 x 1	Ambience, babble, white
BUTReverb	9	An 8-channel spherical array	28	51	Ambience
ACE	7	A 2-channel array (Chromebook),	433	7 x 1 x 2	Ambience, babble, fan
		a 3-channel right-angled triangle array (Mobile),
		an 8-channel linear array (Lin8Ch),
		a 32-channel spherical array (EM32)
LOCATA	1	A 15-channel linear array (DICIT),	492	Moving/static	Ambience
		a 12-channel robot array (Robot head),
		a 32-channel spherical array (Eigenmike)

Quick start

Pretext Task

Preparation

Download datasets to folders according to the following dictionary

.-SAR-SSL
| .-code
| .-data
| .-exp
.-data
  .-SouSig
  | .-wsj0
  |   .-dt
  |   .-et
  |   .-tr
  .-RIR
  | .-DCASE
  | | .-TAU-SRIR_DB
  | | .-TAU-SNoise_DB
  | .-Mesh
  | | .-S32-M441_npy
  | .-MIRDB
  | | .-Impulse_response_Acoustic_Lab_Bar-Ilan_University
  | .-dEchorate
  | | .-dEchorate_database.csv
  | | .-dEchorate_rir.h5
  | | .-dEchorate_annotations.h5
  | | .-dEchorate_noise_gzip7.hdf5
  | | .-dEchorate_babble_gzip7.hdf5
  | | .-dEchorate_silence_gzip7.hdf5
  | .-BUTReverb
  | | .-RIRs
  | .-ACE
  |   .-RIRN
  |   .-Data
  .-SenSig
    .-LOCATA
      .-dev
      .-eval

Install: numpy, scipy, soundfile, gpuRIR, etc.

Data generation

Simulated data

python data_generation_SimulatedSIG_notspecifyroom.py --stage pretrain --wnoise --gpu-id [*]
python data_generation_SimulatedSIG_notspecifyroom.py --stage preval --wnoise --gpu-id [*] 
python data_generation_SimulatedSIG_notspecifyroom.py --stage test --wnoise --gpu-id [*]

Real-world data (DCASE, MeshRIR, MIR, ACE, dEchorate, BUTReverb)

select recorded RIRs and noise signals

python data_generation_MeasuredRIR.py --data-id 0 --data-type rir noise # DCASE
python data_generation_MeasuredRIR.py --data-id 3 --data-type rir noise # ACE
python data_generation_MeasuredRIR.py --data-id 4 --data-type rir noise # dEchorate
python data_generation_MeasuredRIR.py --data-id 5 --data-type rir noise # BUTReverb
python data_generation_MeasuredRIR.py --data-id 1 --data-type rir # MeshRIR 
python data_generation_MeasuredRIR.py --data-id 2 --data-type rir # MIR

generate microphone signals with recorded RIRs and noise signals

python data_generation_SIGfromMeasuredRIR.py --data-id 0 3 4 5 --wnoise --stage pretrain 
python data_generation_SIGfromMeasuredRIR.py --data-id 0 3 4 5 --wnoise --stage preval
python data_generation_SIGfromMeasuredRIR.py --data-id 0 3 4 5 --wnoise --stage test
python data_generation_SIGfromMeasuredRIR.py --data-id 1 2 --stage pretrain 
python data_generation_SIGfromMeasuredRIR.py --data-id 1 2 --stage preval
python data_generation_SIGfromMeasuredRIR.py --data-id 1 2 --stage test

Real-world data (LOCATA)

python data_generation_LOCATA.py --stage pretrain
python data_generation_LOCATA.py --stage preval
python data_generation_LOCATA.py --stage test_pretrain

Simulated data (some instances)
1. uncomment acoustic_scene.dp_mic_signal = [] in class RandomMicSigDatasetOri of data_generation_dataset.py
2. specify room_size, T60, SNR in data_generation_opt.py (default)
3. generate corresponding intances
```
python data_generation_SimulatedSIG_notspecifyroom.py --stage test --wnoise --ins --gpu-id 7 
```

Training

Sepcify the data time version (self.time_ver) and whether training with simulated data (self.pretrain_sim) in class opt_pretrain of opt.py. When using real-world data, first train on simulated data with a default cosine-decay learing rate (initialized with 0.001), and then finetune on real-world data with a learning rate 0.0001.
```
python run_pretrain.py --pretrain --gpu-id [*]
```

Evaluation

Specify test_mode in run_pretrain.py

python run_pretrain.py --test --time [*] --gpu-id [*]

Trained models
- best_model.tar

Downstream Task

Preparation
- the same to pretext task

Data generation

Simulated data

generate RIRs

python data_generation_SimulatedRIR.py --gpu-id [*]

generate microphone signals from RIRs

# room = 2, 4, 8, 16, 32, 64, 128 or 256, and room-trial-id = 16, 8, 4, 2, 1, 1, 1 or 1
python data_generation_SIGfromMeasuredRIR.py --data-id 6 --wnoise --stage train --room 8 --room-trial-id 0 
python data_generation_SIGfromMeasuredRIR.py --data-id 6 --wnoise --stage val --room 20 
python data_generation_SIGfromMeasuredRIR.py --data-id 6 --wnoise --stage test --room 20

Stage	Trials	nRooms	nRIRs/Room	nSrcSig/RIR	nMicSig
train	x16	2	50	2	200
	x8	4	50	2	400
	x4	8	50	2	800
	x2	16	50	2	1600
	x1	32	50	2	3200
	x1	64	50	2	6400
	x1	128	50	2	12800
	x1	256	50	2	25600
val	-	20	50	1	1000
test	-	20	50	4	4000

Real-world data

TDOA estimation

python data_generation_LOCATA.py --stage train
python data_generation_LOCATA.py --stage val 
python data_generation_LOCATA.py --stage test

DRR, T60, C50, absorption coefficient estimation: on-the-fly from selected RIRs and noise signals

Training

Sepcify the data time version (self.time_ver) and whether training with simulated data (downstream_sim) in class opt_downstream of opt.py

Simulated data

# ds-nsimroom = 2, 4, 8, 16, 32, 64, 128 or 256
# ds-trainmode = finetune, lineareval or scratchLOW
python run_downstream.py --ds-train --ds-trainmode finetune --ds-nsimroom 8 --ds-task TDOA --time [*] --gpu-id [*] 
python run_downstream.py --ds-train --ds-trainmode finetune --ds-nsimroom 8 --ds-task DRR T60 C50 ABS --time [*] --gpu-id [*] 

python run_downstream.py --ds-train --ds-trainmode scratchUP --ds-task TDOA --time [*] --gpu-id [*] 
python run_downstream.py --ds-train --ds-trainmode scratchUP --ds-task DRR T60 C50 ABS --time [*] --gpu-id [*]

Real-world data

# ds-trainmode = finetune or scratchLOW
# ds-real-sim-ratio = 1 1, 1 0 or 0 1
python run.py --ds-train --ds-trainmode finetune --ds-real-sim-ratio 1 1 --ds-task TDOA ---time [*] --gpu-id [*]
python run.py --ds-train --ds-trainmode finetune --ds-real-sim-ratio 1 1 --ds-task DRR T60 C50 ABS--time [*] --gpu-id [*]

Evaluation

Specify test mode (test_mode) in run_downstream.py

Simulated data

# ds-nsimroom = 2, 4, 8, 16, 32, 64, 128 or 256
# ds-trainmode = finetune, lineareval or scratchLOW
python run_downstream.py --ds-test --ds-trainmode finetune --ds-nsimroom 8 --ds-task TDOA --time [*] --gpu-id [*] 
python run_downstream.py --ds-test --ds-trainmode finetune --ds-nsimroom 8 --ds-task DRR T60 C50 ABS --time [*] --gpu-id [*] 

python run_downstream.py --ds-test --ds-trainmode scratchUP --ds-task TDOA --time [*] --gpu-id [*] 
python run_downstream.py --ds-test --ds-trainmode scratchUP --ds-task DRR T60 C50 ABS --time [*] --gpu-id [*]

Real-world data

# ds-trainmode = finetune or scratchLOW
# ds-real-sim-ratio = 1 1, 1 0 or 0 1
python run_downstream.py --ds-test --ds-trainmode finetune -ds-real-sim-ratio 1 1 --ds-task TDOA --time [*] --gpu-id [*] 
python run_downstream.py --ds-test --ds-trainmode finetune -ds-real-sim-ratio 1 1 --ds-task DRR T60 C50 ABS --time [*] --gpu-id [*]

Read downstream results (MAEs of TDOA, DRR, T60, C50, SNR, ABS estimation) from saved mat files

python read_dsmat_bslr.py --time [*]
python read_lossmetric_simdata.py
python read_lossmetric_realdata.py

Trained models
- ensemble_model.tar

Others

If OSError: [Errno 24] Too many open files occurs, input the following at the command line

ulimit -n 2048

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{yang2023sarssl,
    author = "Bing Yang and Xiaofei Li",
    title = "Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer",
    booktitle = "arXiv preprint arXiv:2312.00476",
    year = "2023",
    pages = ""}

Licence

MIT

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