11 citations · 11 across the 7 of their papers we have counts for
7 papers
Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset
Eoin Cummins, Zhongyi Huang, Alexandre D'Hooge +2
Existing audio-to-score (A2S) systems primarily focus on classical music, and the application to popular music remains underexplored. This paper first presents the new SheetSage-A2…
Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter-efficient Approach
Pedro Ramoneda, Vsevolod Eremenko, Alexandre D'Hooge +2
Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Neverthe…
Guitar Chord Diagram Suggestion for Western Popular Music
Alexandre d'Hooge, Louis Bigo, Ken Déguernel +1
Chord diagrams are used by guitar players to show where and how to play a chord on the fretboard. They are useful to beginners learning chords or for sharing the hand positions req…
From MIDI to Rich Tablatures: an Automatic Generative System incorporating Lead Guitarists' Fingering and Stylistic choices
Pierluigi Bontempi, Daniele Manerba, Alexandre D'Hooge +1
Although the automatic identification of the optimal fingering for the performance of melodies on fretted string instruments has already been addressed (at least partially) in the…
Modeling Bends in Popular Music Guitar Tablatures
Alexandre D'Hooge, Louis Bigo, Ken Déguernel
Tablature notation is widely used in popular music to transcribe and share guitar musical content. As a complement to standard score notation, tablatures transcribe performance ges…
VaPar Synth -- A Variational Parametric Model for Audio Synthesis
Krishna Subramani, Preeti Rao, Alexandre D'Hooge
With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to mo…
Generative Audio Synthesis with a Parametric Model
Krishna Subramani, Alexandre D'Hooge, Preeti Rao
Use a parametric representation of audio to train a generative model in the interest of obtaining more flexible control over the generated sound.