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20212026
most citedFrequency-Temporal Attention Network for Singing Melody Extraction

22 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2026

A Decomposed Retrieval-Edit-Rerank Framework for Chord Generation

Qiqi He, Dichucheng Li, Xiaoheng Sun +1

Chord generation is an inherently constrained creative task that requires balancing stylistic diversity with music-theoretic feasibility. Existing approaches typically entangle can…

eess.AS2023★ 4 cited

TG-Critic: A Timbre-Guided Model for Reference-Independent Singing Evaluation

Xiaoheng Sun, Yuejie Gao, Hanyao Lin +1

Automatic singing evaluation independent of reference melody is a challenging task due to its subjective and multi-dimensional nature. As an essential attribute of singing voices,…

eess.AS2022★ 5 cited

DEEPCHORUS: A Hybrid Model of Multi-scale Convolution and Self-attention for Chorus Detection

Qiqi He, Xiaoheng Sun, Yi Yu +1

Chorus detection is a challenging problem in musical signal processing as the chorus often repeats more than once in popular songs, usually with rich instruments and complex rhythm…

eess.AS2021★ 3 cited

Musical Tempo Estimation Using a Multi-scale Network

Xiaoheng Sun, Qiqi He, Yongwei Gao +1

Recently, some single-step systems without onset detection have shown their effectiveness in automatic musical tempo estimation. Following the success of these systems, in this pap…

eess.AS2021

Residual Attention Based Network for Automatic Classification of Phonation Modes

Xiaoheng Sun, Yiliang Jiang, Wei Li

Phonation mode is an essential characteristic of singing style as well as an important expression of performance. It can be classified into four categories, called neutral, breathy…

cs.SD2021★ 22 cited

Frequency-Temporal Attention Network for Singing Melody Extraction

Shuai Yu, Xiaoheng Sun, Yi Yu +1

Musical audio is generally composed of three physical properties: frequency, time and magnitude. Interestingly, human auditory periphery also provides neural codes for each of thes…