This code aims at semi-supervised and weakly-supervised sound event detection. The dataset utilized in our experiments is from DCASE (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events), more specifically, from DCASE2018 task4 and DCASE2019 task4. The code embraces two methods we proposed to solve this task: Specialized Decision Surface (SDS) and Disentangled Feature (DF) for weakly-supervised SED and Guided Learning (GL) for weakly-labeled semi-supervised learning.
We're so glad if you're interested in using it for research purpose or DCASE participation. Please don't hesitate to contact us should you have any question.
The dataset utilized in our experiments is from DCASE2018 task4 and DCASE2019 task4. We've encapsulated some interfaces to handle dataset of DCASE task4 and streams to read the data, so this code can be used directly for the future challenge (or as a basis for fine-tuning).
Actually, we exploited it to participate in DCASE2019 task4 and achieve the best performance on the evaluation set among all the submissions to the challenge (challenge results). A challenge-related paper (Guided Learning Convolution System for DCASE 2019 Task 4) is accepeted by DCASE2019 Workshop oral presentation.
We propose specialized decision surface (SDS) and disentangled feature (DF) in paper Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection.
There are mainly 3 contribution in our work:
- The Multiple instance learning (MIL) framework with pooling module and the neural network is commonly utilized for the weakly-supervised learning task, base on which we compare the performances of different MIL approach including the instance-level and embedding-level approach and propose a method to generate frame-level probabilities for the embedding-level approach.
- We propose a specialized decision surface for the embedding-level attention pooling.
- Disentangled feature (DF) is proposed to ease the problem caused by the co-occurrence of categories. We describe it in detail in the paper.
The model architecture utilized in our experiments is the same as the PS-model discussed in the next section.
We propose a method named Guided Learning (GL) forweakly-labeled semi-supervised SED in paper Guided learning for weakly-labeled semi-supervised sound event detection.
Here are 2 model architectures utilized in our experiments:
Keras 2.2.0 (using TensorFlow backend)
TensorFlow 1.8.0
Scripts in "Scripts" directory help quick start.
(Before running scripts, make sure for the data preparation )
You can try to run
to train model with cATP-SDS-DF1 on the dataset of DCASE2018 task4;
Run
to train model with GL on the dataset of DCASE2018 task4;
Run
to train model on the dataset of DCASE2019 task4.
You can find details in example configure ctories (DCASE2018-task4, DCASE2018-task4_semi, DCASE2019-task4).
scripts provides some example scripts to help data preparation:
- gen_feature-2018.sh help extract feature for the dataset of DCASE2018 tas4
- gen_feature-2019.sh help extract feature for the dataset of DCASE2019 tas4
- gen_label-2018.sh help format labels of the dataset of DCASE2018 tas4
- gen_label-2019.sh help format labels of the dataset of DCASE2019 tas4
Before running any example script to extract feature, make sure there are a audio directory to storedoriginal audio files and a feature directory to store output feature. The example script requires data/wav as the audio directory and data/feature as the feature directory.
You can down load audio files from the website of DCASE and store all the audio files in the audio directory before running the scripts.
If you experience any trouble downloading the audio files, you can contact the organizers of DCASE task4. Or send me an E-mail, I'll be glad to help you.
Similarly, before running any example script to generate labels, make sure there is a label directory to store labels. The example script requires data/label as the label directory.
We provide data lists in data/text. The only difference from the original dataset from DCASE2018 task4 is that we provide a file list data/text/all-2018.csv with noisy annotations for the combination of weakly labeled training and unlabeled in domain training set. The noisy annotations, as mentioned in our paper Specialized Decision Surface and DisentangledFeature for Weakly-Supervised Polyphonic Sound Event Detection are obtained roughly by using a PT-model to tag unlabeled data. We release it for reproducing our experiments and we'll be so glad if it is helpful to your research.
Tools to help extract feature and generate labels.
Source codes. See details in the source codes.
run
See details in challenge_results.
Please don't hesitate to contact us should you have any question. You can email me at [email protected] or [email protected].
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