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Found 15 papers, 10 papers with code
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models.
A Foundation Model for Music Informatics
This paper investigates foundation models tailored for music informatics, a domain currently challenged by the scarcity of labeled data and generalization issues.
Scaling Up Music Information Retrieval Training with Semi-Supervised Learning
To our knowledge, this is the first attempt to study the effects of scaling up both model and training data for a variety of MIR tasks.
Textless Speech-to-Music Retrieval Using Emotion Similarity
We introduce a framework that recommends music based on the emotions of speech.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results.
Toward Universal Text-to-Music Retrieval
This paper introduces effective design choices for text-to-music retrieval systems.
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
However, its novelty necessitates a new perspective on how to evaluate such a model.
Ranked #1 on
Music Transcription
on Slakh2100
Emotion Embedding Spaces for Matching Music to Stories
Content creators often use music to enhance their stories, as it can be a powerful tool to convey emotion.
Music Classification: Beyond Supervised Learning, Towards Real-world Applications
The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications.
Multimodal Metric Learning for Tag-based Music Retrieval
In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings.
Mood Classification Using Listening Data
The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music.
Evaluation of CNN-based Automatic Music Tagging Models
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems.
Ranked #1 on
Music Auto-Tagging
on MagnaTagATune (clean)
Music Auto-Tagging
Audio and Speech Processing
Sound
Visualizing and Understanding Self-attention based Music Tagging
Recently, we proposed a self-attention based music tagging model.
Sound Audio and Speech Processing
Toward Interpretable Music Tagging with Self-Attention
In addition, we demonstrate the interpretability of the proposed architecture with a heat map visualization.
Sound Audio and Speech Processing
Transfer Learning of Artist Group Factors to Musical Genre Classification
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy.
