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20212026
most citedVITS-based Singing Voice Conversion System with DSPGAN post-processing for SVCC2023

1 citations · 2 across the 9 of their papers we have counts for

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cs.SD2026

HARP: Harmonic-Aware Residual Partitioning for Neural Audio Codecs

Qiaoyu Yang, Lixing He, Binyue Deng +1

Neural audio codecs with residual vector quantization (RVQ) normally treat all frequencies uniformly, so their codebooks become spectrally entangled. Truncating stages then removes…

cs.SD2026

Poly-SVC: Polyphony-Aware Singing Voice Conversion with Harmonic Modeling

Chen Geng, Meng Chen, Ruohua Zhou +2

Singing Voice Conversion (SVC) aims to transform a source singing voice into a target singer while preserving lyrics and melody. Most existing SVC methods depend on F0 extractors t…

cs.SD2026

LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation

Qi Wang, Zhexu Shen, Meng Chen +4

Vocal-to-accompaniment (V2A) generation, which aims to transform a raw vocal recording into a fully arranged accompaniment, inherently requires jointly addressing an accompaniment…

cs.SD2026

TQCodec: Towards neural audio codec for high-fidelity music streaming

Lixing He, Zhouxuan Chen, Mingshuai Liu +6

We propose TQCodec, a neural audio codec designed for high-bitrate, high-fidelity music streaming. Unlike existing neural codecs that primarily target ultra-low bitrates (<= 16kbps…

cs.SD2025

CycleFlow: Leveraging Cycle Consistency in Flow Matching for Speaker Style Adaptation

Ziqi Liang, Xulong Zhang, Chang Liu +3

Voice Conversion (VC) aims to convert the style of a source speaker, such as timbre and pitch, to the style of any target speaker while preserving the linguistic content. However,…

cs.SD2023★ 1 cited

VITS-based Singing Voice Conversion System with DSPGAN post-processing for SVCC2023

Yiquan Zhou, Meng Chen, Yi Lei +2

This paper presents the T02 team's system for the Singing Voice Conversion Challenge 2023 (SVCC2023). Our system entails a VITS-based SVC model, incorporating three modules: a feat…

cs.SD2023

MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement

Weiming Xu, Zhouxuan Chen, Zhili Tan +5

A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios. Music source separation (M…

cs.SD2021★ 1 cited

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

Xiaobin Zhuang, Huiran Yu, Weifeng Zhao +2

An automatic pitch correction system typically includes several stages, such as pitch extraction, deviation estimation, pitch shift processing, and cross-fade smoothing. However, d…