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20242026
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cs.SD2026

Velocity Prediction in Automatic Guitar Transcription

Jackson Loth, Xavier Riley, Simon Dixon +1

Automatic Music Transcription (AMT) models have achieved a high level of success in polyphonic transcription of various instruments. Velocity, typically a measure of note intensity…

cs.SD2025

Robust Neural Audio Fingerprinting using Music Foundation Models

Shubhr Singh, Kiran Bhat, Xavier Riley +3

The proliferation of distorted, compressed, and manipulated music on modern media platforms like TikTok motivates the development of more robust audio fingerprinting techniques to…

cs.SD2025

Enhanced Automatic Drum Transcription via Drum Stem Source Separation

Xavier Riley, Simon Dixon

Automatic Drum Transcription (ADT) remains a challenging task in MIR but recent advances allow accurate transcription of drum kits with up 5 classes - kick, snare, hi-hats, toms an…

cs.SD2024

GAPS: A Large and Diverse Classical Guitar Dataset and Benchmark Transcription Model

Xavier Riley, Zixun Guo, Drew Edwards +1

We introduce GAPS (Guitar-Aligned Performance Scores), a new dataset of classical guitar performances, and a benchmark guitar transcription model that achieves state-of-the-art per…

cs.SD2024

MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling

Drew Edwards, Xavier Riley, Pedro Sarmento +1

Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to p…

cs.SD2024

Reconstructing the Charlie Parker Omnibook using an audio-to-score automatic transcription pipeline

Xavier Riley, Simon Dixon

The Charlie Parker Omnibook is a cornerstone of jazz music education, described by pianist Ethan Iverson as "the most important jazz education text ever published". In this work we…