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코드

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

https://github.com/MalongTech/research-fad

공식 코드

Representation Sharing for Fast Object Detector Search and Beyond

https://github.com/RussellSB/tt-vae-gan

공식 코드

Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks

https://github.com/YoonjinXD/kadtk

공식 코드

KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation

https://github.com/ahaque/twitch-troll-detection

공식 코드

Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data

https://github.com/biaofuxmu/fast

공식 코드

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference

https://github.com/dinhoitt/bemaganv2

공식 코드

BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation

https://github.com/facebookresearch/flowdec

공식 코드

FlowDec: A flow-based full-band general audio codec with high perceptual quality

https://github.com/hsuneh/dose

공식 코드

DOSE : Drum One-Shot Extraction from Music Mixture

https://github.com/i-need-sleep/mad

공식 코드

Aligning Text-to-Music Evaluation with Human Preferences

https://github.com/jryban/frechet-music-distance

공식 코드

Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation

https://github.com/karchkha/latent-clap-subjective-evaluation

공식 코드

Latent CLAP Loss for Better Foley Sound Synthesis

https://github.com/lzp870/rsfd

공식 코드

Refined Semantic Enhancement towards Frequency Diffusion for Video Captioning

https://github.com/marcojira/stylegan3-melspectrograms

공식 코드

Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrograms

https://github.com/microsoft/fadtk

공식 코드

Adapting Frechet Audio Distance for Generative Music Evaluation

https://github.com/qiuk2/aar

공식 코드

Efficient Autoregressive Audio Modeling via Next-Scale Prediction

https://github.com/researchmm/mm-diffusion

공식 코드

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

https://github.com/schowdhury671/melfusion

공식 코드

MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models

https://github.com/xzwy/msldm

공식 코드

Multi-Source Music Generation with Latent Diffusion

https://github.com/ylwhxht/l4dr

공식 코드

L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection

https://github.com/zhoujingchun03/amsp-uod

공식 코드

AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

https://github.com/acappemin/video-to-audio-and-piano

Enhance Generation Quality of Flow Matching V2A Model via Multi-Step CoT-Like Guidance and Combined Preference Optimization

https://github.com/dcase2024-task7-sound-scene-synthesis/fadtk

Adapting Frechet Audio Distance for Generative Music Evaluation

https://github.com/joezhao527/dm4neuro

Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI

https://github.com/pablebe/gensvs_eval

Adapting Frechet Audio Distance for Generative Music Evaluation

https://github.com/snap-research/GenAU

Taming Data and Transformers for Audio Generation

https://github.com/soham97/pam

Adapting Frechet Audio Distance for Generative Music Evaluation

https://github.com/yc-li20/semi-supervised-training

Addressing Emotion Bias in Music Emotion Recognition and Generation with Frechet Audio Distance

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.