arXiv subjects

Zinu Kim

Publications and source records attributed to Zinu Kim.

2 recordsLinked to original sources

arXiv2026

Tap2Listen: Instant Target Switching for Streaming Audio-Visual Speaker Extraction

Streaming audio-visual target speaker extraction (AV-TSE) uses a target speaker's face to extract the speaker's speech from an input audio mixture in real time. However, existing systems fix the target speaker for an entire clip, while conversation demands frequent and immediate target switches. To address this problem, we propose Tap2Listen, a streaming AV-TSE model designed for instant response to target switches. The model separates computation by modality: the target-agnostic audio encoder processes only the mixture, while the target-specific visual cues of the selected face condition only the decoder. A switch requires only a 0.12ms re-projection and takes effect immediately. We also design component-wise losses: a prediction loss for the encoder and a supervised extraction loss for the decoder. For evaluation, we measure how quickly output quality recovers after a switch. On the standard LRS3-2mix test set, Tap2Listen improves SI-SNRi from 8.85 to 9.68 dB at a comparable parameter count. After a switch to a known face, the model produces full-quality output from the first frame, whereas the baseline needs about 1 second to recover. Finally, we show that the proposed streaming AV-TSE model runs in real time on a single CPU core.

cs.SD↗

arXiv2026

StreamSE: Neural Audio Codec-based Streaming Speech Enhancement for On-device Inference

Recent generative speech enhancement (SE) models achieve high perceptual quality, but their non-causal processing and expensive computation make them difficult to use for streaming, on-device inference. We propose StreamSE, a 20M-parameter streaming SE model that operates in the latent space of a frozen pretrained neural audio codec. At each frame, StreamSE directly regresses a clean continuous latent from the noisy continuous latent while conditioning on its own predictions from previous frames. Enhancement therefore requires a single forward pass per frame and no iterative sampling or look-ahead. We further truncate the codec's residual vector quantizer (RVQ) to its coarse levels, halving the number of tokens per frame while improving robustness to reverberation. StreamSE is the only streaming generative model in our comparison, yet it matches or exceeds the perceptual quality of the offline generative baselines. Furthermore, the complete StreamSE pipeline runs in real time on a single smartphone CPU core. Our results show that continuous latent regression provides a practical route to generative SE under fully causal, on-device streaming constraints.

eess.AS↗