Cheng-i Wang — accepted AI-conference papers

2025

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Ilana Shapiro, Ruanqianqian (Lisa) Huang, Zachary Novack, Cheng-i Wang, Hao-Wen Dong, Taylor Berg-Kirkpatrick +2

IJCAI 2025

Western music is an innately hierarchical system of interacting levels of structure, from fine-grained melody to high-level form. In order to analyze music compositions holistically and at multiple granularities, we propose a unified, hierarchical meta-representation of musical structure called the

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2022

Tonet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music

Ke Chen, Shuai Yu, Cheng-i Wang, Wei Li, Taylor Berg-Kirkpatrick, Shlomo Dubnov

ICASSP 2022accepted

Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information f…

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2020

Deep Autotuner: A Pitch Correcting Network for Singing Performances

Sanna Wager, George Tzanetakis, Cheng-i Wang, Minje Kim

ICASSP 2020accepted

We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and accompaniment. This approach differs from commercial systems, where vocal…

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2019

Intonation: A Dataset of Quality Vocal Performances Refined by Spectral Clustering on Pitch Congruence

Sanna Wager, George Tzanetakis, Stefan Sullivan, Cheng-i Wang, John Shimmin, Minje Kim +1

ICASSP 2019accepted

We introduce the "Intonation" dataset of amateur vocal performances with a tendency for good intonation, collected from Smule, Inc. The dataset can be used for music information retrieval tasks such as autotuning, query by humming, and singing style analysis. It is available upon request on the Stan…

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2018

Singing Style Investigation by Residual Siamese Convolutional Neural Networks

Cheng-i Wang, George Tzanetakis

ICASSP 2018accepted

Investigating singing style is a difficult problem as individual styles are intertwined with melodies from different songs. In this paper, a methodology to investigate singing style is proposed. The proposed approach utilizes convolutional neural networks in a siamese architecture. In addition, we i…

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2016

Structural segmentation with the Variable Markov Oracle and boundary adjustment

Cheng-i Wang, Gautham J. Mysore

ICASSP 2016accepted

For the music structure segmentation task, one wants to solve two co-existing but sometimes contradicting problems; find global repetitive/homogenous structures and locate accurate local change points. In this paper we propose two algorithms to address these two problems. The algorithms can independ…

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2015

Pattern discovery from audio recordings by Variable Markov Oracle: A music information dynamics approach

Cheng-i Wang, Shlomo Dubnov

ICASSP 2015accepted

In this paper, a framework for automatic pattern discovery within an audio recording is proposed. The concept of the proposed framework stems from music information dynamics and is realized by Variable Markov Oracle. Music information dynamics is the research area focusing on information theoretic m…

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