76 citations · 284 across the 16 of their papers we have counts for
19 papers
Masked diffusion enables coherent beat tracking
Francesco Foscarin, Filip Korzeniowski, Richard Vogl
Current neural networks for beat tracking generate invalid outputs, such as consecutive downbeats and erratic tempo changes, even when these are not present in the training data. H…
Diff-VS: Efficient Audio-Aware Diffusion U-Net for Vocals Separation
Yun-Ning, Hung, Richard Vogl +2
While diffusion models are best known for their performance in generative tasks, they have also been successfully applied to many other tasks, including audio source separation. Ho…
Moises-Light: Resource-efficient Band-split U-Net For Music Source Separation
Yun-Ning, Hung, Igor Pereira +1
In recent years, significant advances have been made in music source separation, with model architectures such as dual-path modeling, band-split modules, or transformer layers achi…
Moisesdb: A dataset for source separation beyond 4-stems
Igor Pereira, Felipe Araújo, Filip Korzeniowski +1
In this paper, we introduce the MoisesDB dataset for musical source separation. It consists of 240 tracks from 45 artists, covering twelve musical genres. For each song, we provide…
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…
Evaluating language models of tonal harmony
David R. W. Sears, Filip Korzeniowski, Gerhard Widmer
This study borrows and extends probabilistic language models from natural language processing to discover the syntactic properties of tonal harmony. Language models come in many sh…
A Large-Scale Study of Language Models for Chord Prediction
Filip Korzeniowski, David R. W. Sears, Gerhard Widmer
We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive d…
End-to-End Musical Key Estimation Using a Convolutional Neural Network
Filip Korzeniowski, Gerhard Widmer
We present an end-to-end system for musical key estimation, based on a convolutional neural network. The proposed system not only out-performs existing key estimation methods propo…
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
Matthias Dorfer, Jan Schlüter, Andreu Vall +2
Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query…
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition
Filip Korzeniowski, Gerhard Widmer
Chord recognition systems use temporal models to post-process frame-wise chord preditions from acoustic models. Traditionally, first-order models such as Hidden Markov Models were…
On the Potential of Simple Framewise Approaches to Piano Transcription
Rainer Kelz, Matthias Dorfer, Filip Korzeniowski +3
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-ba…
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
Filip Korzeniowski, Gerhard Widmer
Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have b…
Feature Learning for Chord Recognition: The Deep Chroma Extractor
Filip Korzeniowski, Gerhard Widmer
We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough informatio…
madmom: a new Python Audio and Music Signal Processing Library
Sebastian Böck, Filip Korzeniowski, Jan Schlüter +2
In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, obje…