i work on trustworthy audio — watermarking voices, tracing synthetic speech, and taking models apart from the inside (TTS internals, neural codecs). if an audio metric can be broken, i want to be the one who breaks it... then fixes it.
AudioAuth — dual-watermarking framework: frequency-partitioned model + data watermarks for audio integrity and source attribution (IEEE TBIOM 2026)
WaveVerify — FiLM-generator + MoE-detector watermarking; zero BER under common distortions; beats AudioSeal and WavMark (IJCB 2025)
sourcetrace — codec-residual open-set source tracing of audio deepfakes (EnCodec residual + frozen WavLM-Large; FPR95 1.14% vs published 3.36%)
spanmark — two-route segment localization of partially spoofed speech; cross-corpus SOTA on LlamaPartialSpoof (29.0 EER vs 35.5 baseline)
wavepainter — multimodal LLM-guided diffusion for text-based speech editing; substitution WER 3.48 vs prior best 4.41
cond-ID — speaker unlearning in zero-shot TTS via conditioning-space identity redirection (XTTS-v2, Tortoise-TTS, IndexTTS-1.5)
HiggsAudiov2TokenizerUnofficial — full PyTorch training pipeline for the Higgs Audio V2 tokenizer (HuBERT semantics + DAC + 8-layer RVQ, 960x downsampling)










