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Music Auto-Tagging
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Semantic-Aware Interpretable Multimodal Music Auto-Tagging
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
Parameter-Efficient Transfer Learning for Music Foundation Models
Music auto-tagging in the long tail: A few-shot approach
Music Auto-Tagging with Robust Music Representation Learned via Domain Adversarial Training
WikiMuTe: A web-sourced dataset of semantic descriptions for music audio
Pre-training Music Classification Models via Music Source Separation
Audio Embeddings as Teachers for Music Classification
Multi-Source Contrastive Learning from Musical Audio
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
Contrastive Learning of Musical Representations
TräumerAI: Dreaming Music with StyleGAN
Metric Learning vs Classification for Disentangled Music Representation Learning
How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging
Deep Content-User Embedding Model for Music Recommendation
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Audio-based Distributional Semantic Models for Music Auto-tagging and Similarity Measurement
A Deep Bag-of-Features Model for Music Auto-Tagging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.