https://github.com/IntelLabs/bayesian-torch
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Papers with Code 아카이브
Audio Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 26
- 보존된 평가 기록
- 199
Audio Classification is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Joppewouts/belabBERT
Text-based classification of interviews for mental health -- juxtaposing the state of the art
https://github.com/Logan97117/environmental_sound_classification_1DCNN
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
https://github.com/MindCode-4/code-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-2/code400/tree/main/CRNN
https://github.com/PKU-YuanGroup/LLMBind
https://github.com/PKU-YuanGroup/MoE-LLaVA
https://github.com/PKU-YuanGroup/Video-LLaVA
https://github.com/PaddlePaddle/DeepSpeech
https://github.com/Rishit-dagli/Perceiver
https://github.com/SJTU-YONGFU-RESEARCH-GRP/Lung-Sound-Classification-System-LungSys-I
https://github.com/SJTU-YONGFU-RESEARCH-GRP/LungAttn
https://github.com/SarthakYadav/audax
https://github.com/SarthakYadav/leaf-pytorch
https://github.com/TimmHess/OCDVAEContinualLearning
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
https://github.com/Xianchao-Wu/perceiver-pytorch
https://github.com/Yui010206/CREMA
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/alibaba-damo-academy/FunASR
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
https://github.com/anusfoil/llaqo
Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
https://github.com/asteroid-team/torch-audiomentations
https://github.com/aurianworld/matpac
Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
https://github.com/biboamy/tvsm-dataset
https://github.com/cgaroufis/msspt
https://github.com/chris-lovejoy/youtube-titles-and-transcripts
https://github.com/clarivando/MAX-Audio-Classifier
https://github.com/clementpoiret/Perceiver_MNIST
https://github.com/daisukelab/sound-clf-pytorch
https://github.com/deephdc/audio-classification-tf
https://github.com/edufonseca/icassp19
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
https://github.com/eml-eda/tle-supervised
https://github.com/fac2003/perceiver-multi-modality-pytorch
https://github.com/facebookresearch/multimodal
https://github.com/fmohaghegh/OceanWavePrediction
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
https://github.com/fschmid56/efficientat_hear
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.