MS ECE @ USC, Fall 2026 | Founder, Mantle Sound

Gewenxin Yu

Gewenxin Yu

中文名

Acoustics, Sound, and Speech Processing

about

A researcher based in Jiangsu, China, I work at the intersection of audio signal processing, bioacoustics, and machine learning. I founded Mantle Sound in 2025 and hold an MSc from Queen Mary University of London (QMUL). In Fall 2026, I will join the University of Southern California (USC) to pursue an MS in Electrical and Computer Engineering, with an eye toward doctoral research in the field.

My work spans from developing neural audio codecs for geophone recordings to creating environmental field recordings; since 2023 I have conducted expeditions across China, the UK, France, and Switzerland, most recently documenting the frozen high-altitude soundscape of Yema Haizi (~4,100 m) on the eastern Tibetan Plateau.

research

Mar 2025β€”
E2E Neural Audio Codecs for Geophone and Microphone Recordings

Captured ecological soundscapes and built annotation workflow for soundscape corpus. Trained EnCodec-based neural audio codec with Transformer for ultra-low bitrate compression.

Aug 2023
Enhancing McAdams Coefficient-Based Speaker Anonymisation with Cross-Gender Timbre Transfer

Developed two-stage anonymisation system combining McAdams coefficient DSP and VAE-GAN timbre transfer. Trained on Flickr 8k Audio Caption Corpus. Integrated into ZEBRA framework with improved EER and voice distinctiveness metrics.
(Master's thesis supervised by Dr. Charalampos Saitis, Communication Acoustics Lab at QMUL)

sound works

Jan 2026
Periphyseon

Field recording document from a seasonally frozen lake on the Tibetan Plateau

2024
Eumeswil

Field recordings captured from docks, mountains and coastal areas

Nov 2022
Interactive Sound Installation - Musical Coffee Time

Interactive sound installation that sonifies Moka Pot movements using IanniX, Arduino, and Max/MSP

code

Apr 2023
Denoising of Ecological Bird Song Recordings Using Autoencoder Model

Built autoencoder-based denoising pipeline for bird song recordings from Warblrb10k dataset. Evaluated using SDR/SIR/SAR metrics. Integrated with CNN classifier for improved bird presence classification.

talks

memberships