Huiran Yu

Huiran Yu

2nd year PhD student at University of Rochester

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

Compositions

Here are some composition pieces during my study in Music Engineering and Technology.

publications

KaraTuner: Towards end to end natural pitch correction for singing voice in karaoke

Xiaobin Zhuang, Huiran Yu, Weifeng Zhao, Tao Jiang, Peng Hu

Interspeech 2022, Incheon, South Korea, 2022

An automatic pitch correction system typically includes several stages, such as pitch extraction, deviation estimation, pitch shift processing, and cross-fade smoothing. However, designing these components with strategies often requires domain expertise and they are likely to fail on corner cases. In this paper, we present KaraTuner, an end-to-end neural architecture that predicts pitch curve and resynthesizes the singing voice directly from the tuned pitch and vocal spectrum extracted from the original recordings. Several vital technical points have been introduced in KaraTuner to ensure pitch accuracy, pitch naturalness, timbre consistency, and sound quality. A feed-forward Transformer is employed in the pitch predictor to capture longterm dependencies in the vocal spectrum and musical note. We also develop a pitch-controllable vocoder based on a novel source-filter block and the Fre-GAN architecture. KaraTuner obtains a higher preference than the rule-based pitch correction approach through A/B tests, and perceptual experiments show that the proposed vocoder achieves significant advantages in timbre consistency and sound quality compared with the parametric WORLD vocoder, phase vocoder and CLPC vocoder.

[arXiv] [demo]

What is missing in deep music generation? A study of repetition and structure in popular music

Shuqi Dai, Huiran Yu, Roger B. Dannenberg

23rd Int. Society for Music Information Retrieval (ISMIR), 2022

Structure is one of the most essential aspects of music, and music structure is commonly indicated through repetition. However, the nature of repetition and structure in music is still not well understood, especially in the context of music generation, and much remains to be explored with Music Information Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and American) illustrate important music construction principles: (1) structure exists at multiple hierarchical levels, (2) songs use repetition and limited vocabulary so that individual songs do not follow general statistics of song collections, (3) structure interacts with rhythm, melody, harmony, and predictability, and (4) over the course of a song, repetition is not random, but follows a general trend as revealed by cross-entropy. These and other findings offer challenges as well as opportunities for deep-learning music generation and suggest new formal music criteria and evaluation methods. Music from recent music generation systems is analyzed and compared to human-composed music in our datasets, often revealing striking differences from a structural perspective.

[arXiv]

Note-Level Transcription of Choral Music

Huiran Yu, Zhiyao Duan

25th Int. Society for Music Information Retrieval (ISMIR), 2024

Choral music is a musical activity with one of the largest participant bases, yet it has drawn little attention from automatic music transcription research. The main reasons we argue are due to the lack of data and technical difficulties arise from diverse acoustic conditions and unique properties of choral singing. To address these challenges, in this paper we introduce YouChorale, a novel choral music dataset in a cappella setting curated from the Internet. YouChorale contains 496 real-world recordings in diverse acoustic configurations of choral music from over 100 composers as well as their MIDI scores. In this paper we also propose a Transformer-based framework for note-level transcription of choral music. This framework bypasses the frame-level processing and directly produces a sequence of notes with associated timestamps. Trained on YouChorale, our proposed model achieves state-of-the-art performance in choral music transcription, marking a significant advancement in the field.

[arXiv]

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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