https://github.com/Cocoxili/DCASE2018Task2
공식 코드General audio tagging with ensembling convolutional neural network and statistical features
Papers with Code 아카이브
Audio Tagging
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Audio tagging is a task to predict the tags of audio clips. Audio tagging tasks include music tagging, acoustic scene classification, audio event classification, etc.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JHU-LCAP/CRSTmodel
공식 코드Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures
https://github.com/Jungjee/DcaseNet
공식 코드https://github.com/Kikyo-16/Sound_event_detection
공식 코드Guided learning for weakly-labeled semi-supervised sound event detection
https://github.com/Labbeti/SSLH
공식 코드Comparison of semi-supervised deep learning algorithms for audio classification
https://github.com/LudovicTuncay/hierarchical-label-propagation
공식 코드Hierarchical Label Propagation: A Model-Size-Dependent Performance Booster for AudioSet Tagging
https://github.com/RetroCirce/Zero_Shot_Audio_Source_Separation
공식 코드Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data
https://github.com/Saurabhbhati/DASS
공식 코드DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners
https://github.com/YuanGongND/ast
공식 코드https://github.com/YuanGongND/psla
공식 코드PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation
https://github.com/amphionspace/SA-Eval
공식 코드Solla: Towards a Speech-Oriented LLM That Hears Acoustic Context
https://github.com/arshdeep-singh-boparai/e-panns
공식 코드E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks
https://github.com/audio-westlakeu/audiossl
공식 코드Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
https://github.com/fschmid56/efficientat
공식 코드Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
https://github.com/gbibbo/ai4s-embedded
공식 코드https://github.com/gewu-lab/cross-modal-distillation
공식 코드Robust Cross-Modal Knowledge Distillation for Unconstrained Videos
https://github.com/haoheliu/ontology-aware-audio-tagging
공식 코드https://github.com/jordipons/musicnn
공식 코드musicnn: Pre-trained convolutional neural networks for music audio tagging
https://github.com/jordipons/sklearn-audio-transfer-learning
공식 코드musicnn: Pre-trained convolutional neural networks for music audio tagging
https://github.com/k-h-ismail/dcls-audio
공식 코드Audio classification with Dilated Convolution with Learnable Spacings
https://github.com/kkoutini/passt
공식 코드https://github.com/kumar-shubham-ml/kaggle-birdclef-2021
공식 코드Weakly-Supervised Classification and Detection of Bird Sounds in the Wild.
https://github.com/multitel-ai/urban-sound-classification-and-comparison
공식 코드Urban Sound Classification : striving towards a fair comparison
https://github.com/multitel-ai/urban-sound-tagging
공식 코드https://github.com/nttcslab/m2d
공식 코드Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
https://github.com/pxaris/lc-protonets
공식 코드LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging
https://github.com/rastegah/modnet
공식 코드https://github.com/rhysbv/panns_ndi
공식 코드Integrating IP Broadcasting with Audio Tags: Workflow and Challenges
https://github.com/themoro/efficientsed
공식 코드Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models
https://github.com/topel/SGLThresh
공식 코드Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning
https://github.com/vaslyb/perceptible-music-tagging
공식 코드https://github.com/yanbeic/CCL
공식 코드Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
https://github.com/yongxuUSTC/aDAE_DNN_audio_tagging
공식 코드Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
https://github.com/yongxuUSTC/cnn_rnn_spatial_audio_tagging
공식 코드Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
https://github.com/yuangongnd/cav-mae
공식 코드https://github.com/zhaoyanpeng/vipant
공식 코드Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.