activity
20232026
most citedGAPS: A Large and Diverse Classical Guitar Dataset and Benchmark Transcription Model

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

eess.AS2026

Toward Part-Aware Choral Transcription with singing voice assignment

Hanyu Meng, Zhanhong He, Zixun Guo +1

Single-instrument automatic music transcription (AMT) has advanced substantially, yet choral applications require soprano, alto, tenor, and bass (SATB) to be transcribed as separat…

cs.SD2026

SPHERE: Automatic Music Upmixing via Audio Language Model Post-Training with Spatial Heuristic Rewards

Zixun Guo, Calvin Murdock, Sanjeel Parekh +4

In this paper, we study the task of automatic music upmixing, wherein a system predicts spatial mixing parameters from a multi-stem recording. Different from existing methods that…

cs.SD2026

Equivariant Music Transformer

Zixun Guo, Simon Dixon

Humans recognize a musical passage even when it is shifted in time or transposed in pitch, indicating a notion of equivariance in the representation space. Our analysis, however, s…

cs.SD2025

GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures

Jackson Loth, Pedro Sarmento, Saurjya Sarkar +3

In recent years, the guitar has received increased attention from the music information retrieval (MIR) community driven by the challenges posed by its diverse playing techniques a…

cs.SD2025

Moonbeam: A MIDI Foundation Model Using Both Absolute and Relative Music Attributes

Zixun Guo, Simon Dixon

Moonbeam is a transformer-based foundation model for symbolic music, pretrained on a large and diverse collection of MIDI data totaling 81.6K hours of music and 18 billion tokens.…

cs.SD2024★ 1 cited

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…

eess.AS2023★ 1 cited

Mustango: Toward Controllable Text-to-Music Generation

Jan Melechovsky, Zixun Guo, Deepanway Ghosal +3

The quality of the text-to-music models has reached new heights due to recent advancements in diffusion models. The controllability of various musical aspects, however, has barely…