Music Auto-Tagging
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Benchmarks
Most implemented
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Pre-training Music Classification Models via Music Source Separation
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
Deep Content-User Embedding Model for Music Recommendation
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Papers
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning
Recent advances in music foundation models have improved audio representation learning, yet their effectiveness across diverse musical traditions remains limited. We introduce CultureMERT-95M, a multi-culturally adapted …
Music Auto-TaggingRepresentation LearningTask ArithmeticSemantic-Aware Interpretable Multimodal Music Auto-Tagging
Music auto-tagging is essential for organizing and discovering music in extensive digital libraries. While foundation models achieve exceptional performance in this domain, their outputs often lack interpretability, limi…
Decision MakingMusic Auto-TaggingMusic TaggingM2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Contrastive language-audio pre-training (CLAP) has addressed audio-language tasks such as audio-text retrieval by aligning audio and text in a common feature space. While CLAP addresses general audio-language tasks, its …
Audio captioningAudio ClassificationAudio TaggingAudio to Text Retrieval+13Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned latent space can be further transformed t…
Audio ClassificationAudio TaggingClassificationEnvironmental Sound Classification+9Parameter-Efficient Transfer Learning for Music Foundation Models
More music foundation models are recently being released, promising a general, mostly task independent encoding of musical information. Common ways of adapting music foundation models to downstream tasks are probing and …
Key DetectionMusic Auto-TaggingTransfer LearningMusic auto-tagging in the long tail: A few-shot approach
In the realm of digital music, using tags to efficiently organize and retrieve music from extensive databases is crucial for music catalog owners. Human tagging by experts is labor-intensive but mostly accurate, whereas …
Few-Shot LearningMusic Auto-TaggingTAGTransfer Learning