arXiv Machine Learning
Oct 22

NeuroCodec: EEG-Guided Latent-Domain Generative Model for High-Fidelity Target Speaker Extraction

NeuroCodec is a novel generative framework that uses EEG signals to guide target speaker extraction in multi‑talker environments. It operates in the latent space of a neural audio codec, modeling EEG spatial dependencies with graph convolutions and fusing EEG and speech representations via attention and Mamba‑based temporal modeling. A six‑phase training curriculum and a causal streaming version enable high‑fidelity, 44.1 kHz audio reconstruction up to 20 kHz, outperforming baselines on Cocktail Party and KUL datasets and receiving higher perceptual scores in a listening test.

By Julian Silva, Jaliya Kodithuwakku, Navindu Sirimanna, Shaveen Herath, Wageesha N. Manamperi, Joshua P. Kulasingham
arXiv Machine Learning
Oct 23

Muting Tokens: Exploring Token Sparsification for Efficient Audio Transformers

The paper investigates token sparsification for audio transformers, evaluating various pruning and merging methods across multiple datasets and model sizes. It introduces a new approach that blends attention scores with spectral cues from token embeddings, showing that frequency-aware techniques outperform attention-only methods. The study finds that spectrogram patch intensity-based sparsification is particularly effective for longer audio inputs, underscoring the benefit of incorporating frequency-domain information into sparsification strategies.

By Shreya Sajid, Fabrizio Julian Piva, Hiram Rayo Torres Rodriguez, Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren