DCASE Challenge Task4, Sound Event Detection, Audio Tagging (Classification), Semi-supervised Learning
4 | 5 | ## DCASE Challenge Task4 SOTA 6 | * \[2019/07\] [**Baseline: Sound event detection in domestic environments with weakly labeled data and soundscape synthesis**](https://inria.hal.science/hal-02160855/) 7 | 8 | * \[2020/07\] [**CONVOLUTION-AUGMENTED TRANSFORMER FOR SEMI-SUPERVISED SOUND EVENT DETECTION**](https://dcase.community/documents/challenge2020/technical_reports/DCASE2020_Miyazaki_108.pdf) 9 | 10 | * \[2021/07\] [**ZHENG USTC TEAM’S SUBMISSION FOR DCASE2021 TASK4 - SEMI-SUPERVISED SOUND EVENT DETECTION**](https://dcase.community/documents/challenge2021/technical_reports/DCASE2021_Zheng_110_t4.pdf) 11 | 12 | * \[2022/07\] [**PRE-TRAINING AND SELF-TRAINING FOR SOUND EVENT DETECTION IN DOMESTIC ENVIRONMENTS**](https://dcase.community/documents/challenge2022/technical_reports/DCASE2022_Ebbers_125_t4.pdf) 13 | 14 | * [⭐mine⭐]\[2023/07\] [**Single Model Track: SEMI-SUPERVISED LEARNING-BASED SOUND EVENT DETECTION USING FREQUENCY DYNAMIC CONVOLUTION WITH LARGE KERNEL ATTENTION FOR DCASE CHALLENGE 2023 TASK 4**](https://dcase.community/documents/challenge2023/technical_reports/DCASE2023_Kim_82_t4a.pdf) 15 | 16 | * \[2023/07\] [**Ensemble Model Track: SOUND EVENT DETECTION WITH WEAK PREDICTION FOR DCASE 2023 CHALLENGE TASK4A**](https://dcase.community/documents/challenge2023/technical_reports/DCASE2023_Zhang_63_t4a.pdf) 17 | 18 | ## Sound Event Detection 19 | * \[2021/01\] [**Polyphonic Sound Event Detection Based on Residual Convolutional Recurrent Neural Network With Semi-Supervised Loss Function**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9312148) 20 | 21 | 22 | * \[2022/03\] [**RCT: RANDOM CONSISTENCY TRAINING 23 | FOR SEMI-SUPERVISED SOUND EVENT DETECTION**](https://arxiv.org/pdf/2110.11144.pdf) 24 | 25 | * \[2022/05\] [**FILTERAUGMENT: AN ACOUSTIC ENVIRONMENTAL DATA AUGMENTATION METHOD**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747680) 26 | 27 | * \[2022/07\] [**Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection**](https://arxiv.org/pdf/2203.15296.pdf) 28 | 29 | * [⭐mine⭐]\[2022/10\] [**Sound Event Detection Using Attention and Aggregation-Based Feature Pyramid Network**](https://ieeexplore.ieee.org/document/9943734) 30 | 31 | * \[2023/01\] [**THRESHOLD INDEPENDENT EVALUATION OF SOUND EVENT DETECTION SCORES**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747556) 32 | 33 | 34 | * [⭐mine⭐]\[2023/01\] [**Sound Event Detection Using EfficientNet-B2 with an Attentional Pyramid Network**](https://ieeexplore.ieee.org/document/10043590) 35 | 36 | * \[2023/05\] [**AST-SED: AN EFFECTIVE SOUND EVENT DETECTION METHOD BASED ON AUDIO SPECTROGRAM TRANSFORMER**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10096853) 37 | 38 | * \[2023/05\] [**MULTI-DIMENSIONAL FREQUENCY DYNAMIC CONVOLUTION WITH CONFIDENT MEAN TEACHER FOR SOUND EVENT DETECTION**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10096306) 39 | 40 | * \[2024/06\] [**PUSHING THE LIMIT OF SOUND EVENT DETECTION WITH MULTI-DILATED FREQUENCY DYNAMIC CONVOLUTION**](https://arxiv.org/pdf/2406.13312) 41 | 42 | 43 | ## Audio Tagging 44 | * \[2020/10\] [**PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9229505) 45 | 46 | * \[2021/10\] [**PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9576629) 47 | 48 | * \[2021/04\] [**AST: Audio Spectrogram Transformer**](https://arxiv.org/pdf/2104.01778.pdf) 49 | 50 | * \[2022/03\] [**Efficient Training of Audio Transformers with Patchout**](https://arxiv.org/pdf/2110.05069.pdf) 51 | 52 | * \[2022/04\] [**AudioTagging Done Right: 2nd comparison of deep learning methods for environmental sound classification**](https://arxiv.org/pdf/2203.13448.pdf) 53 | 54 | * \[2022/05\] [**HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9746312) 55 | 56 | * \[2022/06\] [**SSAST: Self-Supervised Audio Spectrogram Transformer**](https://ojs.aaai.org/index.php/AAAI/article/view/21315) 57 | 58 | * \[2022/11\] [**ATST: Audio Representation Learning with Teacher-Student Transformer**](https://arxiv.org/pdf/2204.12076) 59 | 60 | * \[2022/12\] [**BEATs: Audio Pre-Training with Acoustic Tokenizers**](https://arxiv.org/pdf/2212.09058.pdf) 61 | 62 | 63 | ## Semi-supervised Learning 64 | 65 | * \[2017/06\] [**Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results**](https://proceedings.neurips.cc/paper_files/paper/2017/file/68053af2923e00204c3ca7c6a3150cf7-Paper.pdf) 66 | 67 | * \[2020/06\] [**Self-Training With Noisy Student Improves ImageNet Classification**](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xie_Self-Training_With_Noisy_Student_Improves_ImageNet_Classification_CVPR_2020_paper.pdf) 68 | 69 | * \[2021/05\] [**Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning**](https://ojs.aaai.org/index.php/AAAI/article/view/16852) 70 | 71 | * \[2021/12\] [**FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling**](https://proceedings.neurips.cc/paper_files/paper/2021/file/995693c15f439e3d189b06e89d145dd5-Paper.pdf) 72 | --------------------------------------------------------------------------------