From the 1 of 5 linked papers with an AI index.
5 papers
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
The paper introduces Cortical-SSM, a deep state space model that decodes motor imagery EEG signals by integrating temporal, spatial, and frequency information, achieving higher acc…
ELSA: Acoustic Event-Level Semantic Alignment for Fine-Grained Reference-Free Text-to-Audio Evaluation
Shuntaro Suzuki, Kento Tokura, Daichi Yashima +3
Text-to-audio (TTA) generation, synthesizing audio from natural language, has been widely studied for its ability to capture precise user intent. To effectively advance TTA models,…
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Daichi Yashima, Shuhei Kurita, Yusuke Oda +3
In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging because of their intricate…
MEGState: Phoneme Decoding from Magnetoencephalography Signals
Shuntaro Suzuki, Chia-Chun Dan Hsu, Yu Tsao +1
Decoding linguistically meaningful representations from non-invasive neural recordings remains a central challenge in neural speech decoding. Among available neuroimaging modalitie…
Condition-Invariant fMRI Decoding of Speech Intelligibility with Deep State Space Model
Ching-Chih Sung, Shuntaro Suzuki, Francis Pingfan Chien +2
Clarifying the neural basis of speech intelligibility is critical for computational neuroscience and digital speech processing. Recent neuroimaging studies have shown that intellig…