Discriminative Sounding Objects Localization

Code for our NeurIPS 2020 paper Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching (The previous title is Learning to Discriminatively Localize Sounding Objects in a Cocktail-party Scenario). The code is implemented on PyTorch with python3.

The code for TPAMI version can be found here: https://github.com/GeWu-Lab/CSOL_TPAMI2021.

Requirements

• PyTorch 1.1
• torchvision
• scikit-learn
• librosa
• Pillow
• opencv

Running Procedure

For experiments on Music or AudioSet-instrument, the training and evaluation procedures are similar, respectively under the folder music-exp and audioset-instrument. Here, we take the experiments on Music dataset as an example.

Data Preparation

• Download dataset, e.g., MUSIC, and split into training/validation/testing set. Specifically, for the training@stage_one, please use the solo_training_1.txt. For the training@stage_two, we use the the music clip in solo_training_2.txt to synthesize the cocktail-party scenarios.

• Extract frames at 4 fps by running

  python3 data/cut_video.py
  

• Extract 1-second audio clips and turn into Log-Mel-Spectrogram by running

  python3 data/cut_audio.py
  

The sounding object bounding box annotations on solo and duet are stored in music-exp/solotest.json and music-exp/duettest.json, and the data and annotations of synthetic set are available at https://zenodo.org/record/4079386#.X4PFodozbb2 . And the Audioset-instrument balanced subset bounding box annotations are in audioset-instrument/audioset_box.json

Training

Stage one

training_stage_one.py [-h]
optional arguments:
[--batch_size] training batchsize
[--learning_rate] learning rate
[--epoch] total training epoch
[--evaluate] only do testing or also training
[--use_pretrain] whether to initialize from ckpt
[--ckpt_file] the ckpt file path to be resumed
[--use_class_task] whether to use localization-classification alternative training
[--class_iter] training iterations for classification of each epoch
[--mask] mask threshold to determine whether is object or background
[--cluster] number of clusters for discrimination

python3 training_stage_one.py

After training of stage one, we will get the cluster pseudo labels and object dictionary of different classes in the folder ./obj_features, which is then used in the second stage training as category-aware object representation reference.

Stage two

training_stage_two.py [-h]
optional arguments:
[--batch_size] training batchsize
[--learning_rate] learning rate
[--epoch] total training epoch
[--evaluate] only do testing or also training
[--use_pretrain] whether to initialize from ckpt
[--ckpt_file] the ckpt file path to be resumed

python3 training_stage_two.py

Evaluation

Stage one

We first generate localization results and save then as a pkl file, then calculate metrics, IoU and AUC and also generate visualizations, by running

python3 training_stage_one.py --mode test --use_pretrain 1 --ckpt_file your_ckpt_file_path
python3 tools.py

Stage two

For evaluation of stage two, i.e., class-aware sounding object localization in multi-source scenes, we first match the cluster pseudo labels generated in stage one with gt labels to accordingly assign one object category to each center representation in the object dictionary by running

python3 match_cluster.py

It is necessary to manually ensure there is one-to-one matching between object category and each center representation.

Then we generate the localization results and calculate metrics, CIoU AUC and NSA, by running

python3 test_stage_two.py
python3 eval.py

Results

The two tables respectively show our model's performance on single-source and multi-source scenarios.

The following figures show the category-aware localization results under multi-source scenes. The green boxes mean the sounding objects while the red boxes are silent ones.