Multi-level graph learning (MLGL)

Citation

Please consider citing our paper as

@INPROCEEDINGS{10446633,
  author={Hou, Yuanbo and Ren, Qiaoqiao and Song, Siyang and Song, Yuxin and Wang, Wenwu and Botteldooren, Dick},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Multi-Level Graph Learning For Audio Event Classification And Human-Perceived Annoyance Rating Prediction}, 
  year={2024},
  volume={},
  number={},
  pages={716-720},
  keywords={Analytical models;Statistical analysis;Working environment noise;Semantics;Urban areas;Signal processing;Task analysis;Noise;annoyance;audio event classification;annoyance rating prediction;multi-level graph learning},
  doi={10.1109/ICASSP48485.2024.10446633}}

The proposed lightweight attention-fused multi-level graph learning (MLGL).

Fig. 2: (a) Correlation between node embeddings of multiple LcGs.

Fig. 2: (b) Correlation between node embeddings of the GcG.

Run models

1) Unzip the Dataset under the application folder

2) Unzip the Pretrained_models under the application folder

3) Enter the application folder: cd application

1) Multi-level predictions of MLGL for AEC and ARP

python inference_MLGL_3_levels.py
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Loading data time: 0.656 s
Split development data to 2200 training and 245 validation data and 445 test data. 
Number of 445 audios in testing
AEC 24 classes of fAEs:
	level 1 Acc: 0.9191011235955057,  AUC: 0.9179580247709974
	level 2 Acc: 0.9190074906367042,  AUC: 0.9200157437353879
	level 3 Acc: 0.9195692883895131,  AUC: 0.9206666156627457
AEC 7 classes of cAEs:
	level 1 Acc: 0.8471910112359551,  AUC: 0.9098863636363642
	level 2 Acc: 0.8497592295345104,  AUC: 0.9103030303030312
	level 3 Acc: 0.849438202247191,  AUC: 0.9121969696969697
ARP:
	level 1 mse_loss: 1.0618256704746445,  mae_loss: 0.7669948252774356, r2: 0.4519510651373152
	level 2 mse_loss: 0.9643998833119938,  mae_loss: 0.7172525859039821, r2: 0.5022362488236182
	level 3 mse_loss: 0.9395196263112058,  mae_loss: 0.7059113823644231, r2: 0.5150779032755175

Model parameters

python model_params.py  
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Parameters num: 4.146464 M

Comparison of fusion methods

1) MLGL_addition

python inference_other_models.py  -model MLGL_addition
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Loading data time: 0.672 s
Split development data to 2200 training and 245 validation data and 445 test data.
Number of 445 audios in testing
AEC:
        Acc: 0.9166666666666666,  AUC: 0.8969593335604743
ARP:
        mse_loss: 1.2473996856818441, mae_loss: 0.845888664406337, r2: 0.35616920169165645

2) MLGL_concate

python inference_other_models.py -model MLGL_concate 
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Loading data time: 0.750 s
Split development data to 2200 training and 245 validation data and 445 test data.
Number of 445 audios in testing
AEC:
        Acc: 0.9168539325842696,  AUC: 0.9025347073227502
ARP:
        mse_loss: 1.1479789709826083, mae_loss: 0.8176497273498707, r2: 0.4074840439574746

3) MLGL_Hadamard

python inference_other_models.py -model MLGL_Hadamard 
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Loading data time: 0.766 s
Split development data to 2200 training and 245 validation data and 445 test data.
Number of 445 audios in testing
AEC:
        Acc: 0.9173220973782772,  AUC: 0.9082991382977522
ARP:
        mse_loss: 1.2815268686672419, mae_loss: 0.8723713762894105, r2: 0.3385548542473622

4) MLGL_Gating

python inference_other_models.py -model MLGL_Gating 
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Loading data time: 0.673 s
Split development data to 2200 training and 245 validation data and 445 test data.
Number of 445 audios in testing
AEC:
        Acc: 0.9175093632958802,  AUC: 0.9065028568144324
ARP:
        mse_loss: 1.14128984851321, mae_loss: 0.8130632776196084, r2: 0.4109365564992755

5) MLGL_attention

python inference_other_models.py -model MLGL_attention 
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Loading data time: 0.673 s
Split development data to 2200 training and 245 validation data and 445 test data.
Number of 445 audios in testing
AEC:
        Acc: 0.9195692883895131,  AUC: 0.9206666156627457
ARP:
        mse_loss: 0.9395196263112058, mae_loss: 0.7059113823644231, r2: 0.5150779032755175