7 citations · 8 across the 2 of their papers we have counts for
10 papers
Supervised and Unsupervised Learning of Audio Representations for Music Understanding
Matthew C. McCallum, Filip Korzeniowski, Sergio Oramas +2
In this work, we provide a broad comparative analysis of strategies for pre-training audio understanding models for several tasks in the music domain, including labelling of genre,…
Artist Similarity with Graph Neural Networks
Filip Korzeniowski, Sergio Oramas, Fabien Gouyon
Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid a…
Mood Classification Using Listening Data
Filip Korzeniowski, Oriol Nieto, Matthew McCallum +3
The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting…
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
Filip Korzeniowski, Gerhard Widmer
Common temporal models for automatic chord recognition model chord changes on a frame-wise basis. Due to this fact, they are unable to capture musical knowledge about chord progres…
Genre-Agnostic Key Classification With Convolutional Neural Networks
Filip Korzeniowski, Gerhard Widmer
We propose modifications to the model structure and training procedure to a recently introduced Convolutional Neural Network for musical key classification. These modifications ena…
Improved Chord Recognition by Combining Duration and Harmonic Language Models
Filip Korzeniowski, Gerhard Widmer
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 segm…