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Found 11 papers, 8 papers with code
Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio
APNet allows prototypes' reconstruction to waveforms for interpretability relying on the nearest training data samples.
mir_ref: A Representation Evaluation Framework for Music Information Retrieval Tasks
Music Information Retrieval (MIR) research is increasingly leveraging representation learning to obtain more compact, powerful music audio representations for various downstream MIR tasks.
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.
Enriched Music Representations with Multiple Cross-modal Contrastive Learning
Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information.
Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649, 091tracks and 148, 826 associated playlists annotated by 30, 652 different tags.
The Freesound Loop Dataset and Annotation Tool
We present the Freesound Loop Dataset (FSLD), a new large-scale dataset of music loops annotated by experts.
Audio and Speech Processing Sound
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
TensorFlow Audio Models in Essentia
Essentia is a reference open-source C++/Python library for audio and music analysis.
Artist and style exposure bias in collaborative filtering based music recommendations
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles.
How 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.
Skip prediction using boosting trees based on acoustic features of tracks in sessions
The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not.
