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Sound Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AndreyGuzhov/AudioCLIP
공식 코드https://github.com/AndreyGuzhov/ESResNet
공식 코드ESResNet: Environmental Sound Classification Based on Visual Domain Models
https://github.com/JNAIC/PIPMN
공식 코드Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
https://github.com/Labbeti/SSLH
공식 코드Comparison of semi-supervised deep learning algorithms for audio classification
https://github.com/RSC-Toolkit/rsc-ensemble-kd
공식 코드https://github.com/ai-techsystems/deepC
공식 코드Tiny Transformers for Environmental Sound Classification at the Edge
https://github.com/alireza-nasiri/SoundCLR
공식 코드SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
https://github.com/allholy/bsd10k
공식 코드Heterogeneous sound classification with the Broad Sound Taxonomy and Dataset
https://github.com/bootphon/learnable-strf
공식 코드Learning spectro-temporal representations of complex sounds with parameterized neural networks
https://github.com/chenhao1umbc/WSCDL
공식 코드Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
https://github.com/chumingqian/CycleGuardian
공식 코드https://github.com/corticph/MSTmodel
공식 코드https://github.com/cwx-worst-one/eat
공식 코드EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
https://github.com/deegy666/add-rsc
공식 코드Adaptive Differential Denoising for Respiratory Sounds Classification
https://github.com/eacevedo1/audiotext-contextda
공식 코드https://github.com/ear-team/bambird
공식 코드Unsupervised classification to improve the quality of a bird song recording dataset
https://github.com/epic-kitchens/epic-sounds-annotations
공식 코드https://github.com/fadymedhat/MCLNN
공식 코드Masked Conditional Neural Networks for Environmental Sound Classification
https://github.com/fadymedhat/YorNoise
공식 코드Masked Conditional Neural Networks for Environmental Sound Classification
https://github.com/gariscat/housex
공식 코드HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry
https://github.com/gitmehrdad/face
공식 코드Face: Fast, Accurate and Context-Aware Audio Annotation and Classification
https://github.com/glam-imperial/soundprototypecriticism
공식 코드Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis
https://github.com/hildeingvildhummel/uatr
공식 코드https://github.com/ilyassmoummad/scl_icbhi2017
공식 코드Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
https://github.com/ilyassmoummad/ssl4birdsounds
공식 코드Self-Supervised Learning for Few-Shot Bird Sound Classification
https://github.com/junwoopark92/BG-Gun-Sound-Dataset
공식 코드Enemy Spotted: in-game gun sound dataset for gunshot classification and localization
https://github.com/justinsalamon/UrbanSound8K-JAMS
공식 코드Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound
공식 코드Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
https://github.com/kaen2891/bts
공식 코드BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification
https://github.com/kaen2891/stethoscope-guided_supervised_contrastive_learning
공식 코드https://github.com/mhealthbuet/bmd-hs-dataset
공식 코드https://github.com/mohaimenz/acdnet
공식 코드https://github.com/multitel-ai/urban-sound-classification-and-comparison
공식 코드Urban Sound Classification : striving towards a fair comparison
https://github.com/pritamqu/CrissCross
공식 코드Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity
https://github.com/pritamqu/XKD
공식 코드XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.