most citedA Fully Convolutional Deep Auditory Model for Musical Chord Recognition

76 citations · 242 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SD201666 cited

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…

cs.LG201676 cited

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…

cs.SD20167 cited

Live Score Following on Sheet Music Images

Matthias Dorfer, Andreas Arzt, Sebastian Böck +2

In this demo we show a novel approach to score following. Instead of relying on some symbolic representation, we are using a multi-modal convolutional neural network to match the i…

cs.SD20165 cited

Towards End-to-End Audio-Sheet-Music Retrieval

Matthias Dorfer, Andreas Arzt, Gerhard Widmer

This paper demonstrates the feasibility of learning to retrieve short snippets of sheet music (images) when given a short query excerpt of music (audio) -- and vice versa --, witho…

cs.SD201659 cited

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…

cs.LG201618 cited

Towards Score Following in Sheet Music Images

Matthias Dorfer, Andreas Arzt, Gerhard Widmer

This paper addresses the matching of short music audio snippets to the corresponding pixel location in images of sheet music. A system is presented that simultaneously learns to re…