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Yimin Cao

dblp:173/0947 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › speech coding
neural speech codec
1.012026
SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs · AAAI 2026
Audio and music processing
speech coding
1.012026
SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs · AAAI 2026
Audio and music processing › audio representation learning
speech representation
1.012026
SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs · AAAI 2026
Audio and music processing
speech synthesis
0.312026
SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs · AAAI 2026

Methods — techniques the papers use, named apart from their topics

semantic anchoring · 1.0residual vector quantization · 1.0mHuBERT · 1.0asymmetric dual quantization · 1.0
YearPublicationVenuePosition
2026 SACodec: Asymmetric Quantization with Semantic Anchoring for Low-Bitrate High-Fidelity Neural Speech Codecs
abstract
Neural Speech Codecs face a fundamental trade-off at low bitrates: preserving acoustic fidelity often compromises semantic richness. To address this, we introduce SACodec, a novel codec built upon an asymmetric dual-quantizer that employs our proposed Semantic Anchoring mechanism. This design strategically decouples the quantization of Semantic and Acoustic details. The semantic anchoring is achieved via a lightweight projector that aligns acoustic features with a frozen, large-scale mHuBERT codebook, injecting linguistic priors while guaranteeing full codebook utilization. Sequentially, for acoustic details, a residual activation module with SimVQ enables a single-layer quantizer (acoustic path) to faithfully recover fine-grained information. At just 1.5 kbps, SACodec establishes a new state of the art by excelling in both fidelity and semantics: subjective listening tests confirm that its reconstruction quality is perceptually highly comparable to ground-truth audio, while its tokens demonstrate substantially improved semantic richness in downstream tasks. This work suggests that assigning specialized semantic quantizers to distinct information streams offers an effective path to reconcile the long-standing trade-off between fidelity, semantics, and modeling simplicity in low-bitrate speech tokenization.
Zhongren Dong, Jing Han 0010, Xiaojun Mo, Yimin Cao, Zixing Zhang 0001
AAAI6
2017 Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines
Juntao Li 0001, Yimin Cao, Huimin Xiao
Neural Process. Lett.2
2016 Weighted doubly regularized support vector machine and its application to microarray classification with noise
Juntao Li 0001, Yimin Cao, Cunshuan Xu
Neurocomputing3