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Found 13 papers, 6 papers with code
Moisesdb: A dataset for source separation beyond 4-stems
To facilitate the adoption of this dataset, we publish an easy-to-use Python library to download, process and use MoisesDB.
Supervised and Unsupervised Learning of Audio Representations for Music Understanding
We find that restricting the domain of the pre-training dataset to music allows for training with smaller batch sizes while achieving state-of-the-art in unsupervised learning -- and in some cases, supervised learning -- for music understanding.
Artist Similarity with Graph Neural Networks
Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music.
Mood Classification Using Listening Data
The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music.
Improved Chord Recognition by Combining Duration and Harmonic Language Models
Chord recognition systems typically comprise an acoustic model that predicts chords for each audio frame, and a temporal model that casts these predictions into labelled chord segments.
Genre-Agnostic Key Classification With Convolutional Neural Networks
Finally, we investigate the model's performance on short excerpts of audio.
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
Common temporal models for automatic chord recognition model chord changes on a frame-wise basis.
A Large-Scale Study of Language Models for Chord Prediction
We conduct a large-scale study of language models for chord prediction.
End-to-End Musical Key Estimation Using a Convolutional Neural Network
We present an end-to-end system for musical key estimation, based on a convolutional neural network.
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition
Chord recognition systems use temporal models to post-process frame-wise chord preditions from acoustic models.
On the Potential of Simple Framewise Approaches to Piano Transcription
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription.
Feature Learning for Chord Recognition: The Deep Chroma Extractor
We explore frame-level audio feature learning for chord recognition using artificial neural networks.
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.
