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Sound Event Detection

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보존된 평가 기록: 25

Sound Event Detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.

Source: A report on sound event detection with different binaural features

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/965694547/hybrid-system-of-frame-wise-model-and-sedt

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A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4

https://github.com/Audio-WestlakeU/ATST-SED

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Fine-tune the pretrained ATST model for sound event detection

https://github.com/Audio-WestlakeU/RCT-Random-Consistency-Training

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RCT: Random Consistency Training for Semi-supervised Sound Event Detection

https://github.com/BirdVox/birdvoxdetect

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Robust sound event detection in bioacoustic sensor networks

https://github.com/ChrisIck/DCASE_Synth_Data

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Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and Localization

https://github.com/Harper812/FFDConv

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Full-frequency dynamic convolution: a physical frequency-dependent convolution for sound event detection

https://github.com/JHU-LCAP/CRSTmodel

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Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures

https://github.com/Jungjee/DcaseNet

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DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events

https://github.com/Kikyo-16/Sound_event_detection

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Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection

https://github.com/MadryLab/cifar10_challenge

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Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/MadryLab/mnist_challenge

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Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/RetroCirce/Zero_Shot_Audio_Source_Separation

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Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data

https://github.com/Toshiba-China-RDC/dcase20_task4

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Couple Learning for semi-supervised sound event detection

https://github.com/Toshiba-RDC/dcase20_task4

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Couple Learning for semi-supervised sound event detection

https://github.com/ankitshah009/WALNet-Weak_Label_Analysis

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A Closer Look at Weak Label Learning for Audio Events

https://github.com/apple-yinhan/noise-robust-sed

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Leveraging LLM and Text-Queried Separation for Noise-Robust Sound Event Detection

https://github.com/apple-yinhan/tq-sed

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Exploring Text-Queried Sound Event Detection with Audio Source Separation

https://github.com/arjunp17/MemoryControlled-MultiheadSelfAtt

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Memory Controlled Sequential Self Attention for Sound Recognition

https://github.com/audio-westlakeu/audiossl

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Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks

https://github.com/audio-westlakeu/rct

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RCT: Random Consistency Training for Semi-supervised Sound Event Detection

https://github.com/c4dm/dcase-few-shot-bioacoustic

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Few-shot bioacoustic event detection at the DCASE 2022 challenge

https://github.com/cai525/transformer4sed

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MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection

https://github.com/cpjku/cpjku_dcase24

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Multi-Iteration Multi-Stage Fine-Tuning of Transformers for Sound Event Detection with Heterogeneous Datasets

https://github.com/dberghi/av-seld

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Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection

https://github.com/dcase-repo/desed_task

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The impact of non-target events in synthetic soundscapes for sound event detection

https://github.com/dr-costas/SEDLM

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Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling

https://github.com/dr-costas/dnd-sed

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Sound Event Detection with Depthwise Separable and Dilated Convolutions

https://github.com/drylbear/soundscapeCityClassification

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City classification from multiple real-world sound scenes

https://github.com/edufonseca/icassp19

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Learning Sound Event Classifiers from Web Audio with Noisy Labels

https://github.com/elegan23/hypernets

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PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions

https://github.com/fgnt/pb_sed

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Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection

https://github.com/fgnt/sed_scores_eval

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Threshold Independent Evaluation of Sound Event Detection Scores

https://github.com/frednam93/FDY-SED

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Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection

https://github.com/frednam93/FilterAugSED

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Heavily Augmented Sound Event Detection utilizing Weak Predictions

https://github.com/frednam93/JiTTER-SED

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JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection

https://github.com/frednam93/MDFD-SED

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Diversifying and Expanding Frequency-Adaptive Convolution Kernels for Sound Event Detection

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