Papers Music Auto-Tagging
“Music Auto-Tagging” 태그가 달린 논문 22편 · 필터 해제
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 LearningMusic Auto-Tagging with Robust Music Representation Learned via Domain Adversarial Training
Music auto-tagging is crucial for enhancing music discovery and recommendation. Existing models in Music Information Retrieval (MIR) struggle with real-world noise such as environmental and speech sounds in multimedia co…
Information RetrievalMusic Auto-TaggingMusic Information RetrievalRetrievalWikiMuTe: A web-sourced dataset of semantic descriptions for music audio
Multi-modal deep learning techniques for matching free-form text with music have shown promising results in the field of Music Information Retrieval (MIR). Prior work is often based on large proprietary data while public…
ArticlesCross-Modal RetrievalInformation RetrievalMusic Auto-Tagging+3Pre-training Music Classification Models via Music Source Separation
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks. To this end, we first pre-train U-Net networks und…
ClassificationGenre classificationMusic Auto-TaggingMusic Classification+3Audio Embeddings as Teachers for Music Classification
Music classification has been one of the most popular tasks in the field of music information retrieval. With the development of deep learning models, the last decade has seen impressive improvements in a wide range of c…
ClassificationInformation RetrievalInstrument RecognitionKnowledge Distillation+5Multi-Source Contrastive Learning from Musical Audio
Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated…
CoLAContrastive LearningGenre classificationMusic Auto-Tagging+4Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM…
ClassificationGenre classificationMusic Auto-TaggingMusic Genre ClassificationContrastive Learning of Musical Representations
While deep learning has enabled great advances in many areas of music, labeled music datasets remain especially hard, expensive, and time-consuming to create. In this work, we introduce SimCLR to the music domain and con…
Contrastive LearningLinear evaluationMusic Auto-TaggingMusic Classification+1TräumerAI: Dreaming Music with StyleGAN
The goal of this paper to generate a visually appealing video that responds to music with a neural network so that each frame of the video reflects the musical characteristics of the corresponding audio clip. To achieve …
Music Auto-TaggingMetric Learning vs Classification for Disentangled Music Representation Learning
Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two central methods for representation learning i…
ClassificationDisentanglementGeneral ClassificationInformation Retrieval+6How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging
Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning. In particular, many…
Information RetrievalMusic Auto-TaggingMusic Information RetrievalRetrievalDeep Content-User Embedding Model for Music Recommendation
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborati…
Collaborative FilteringmodelMusic Auto-TaggingMusic Recommendation+1Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1…
General Classificationimage-classificationMusic Auto-TaggingMulti-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags…
General Classificationimage-classificationImage ClassificationMusic Auto-Tagging+1Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains. This approach was ap…
Music Auto-TaggingMusic Classification