The paper introduces CaReCoS, a benchmark for multimodal reasoning over medical acoustic spectrograms, and evaluates the performance of vision and omni models, finding a maximum accuracy of 51.2%.

This paper investigates neural activity during five auditory conditions using EEG recordings from a 5-year-old participant, revealing condition-specific modulation of neural oscillatory activity and d…

This study investigates the feasibility of pediatric asthma detection in emergency departments using breath sound recordings and machine learning with pretrained self-supervised speech representation…

This paper proposes a data-driven method for automatic blind audio equalization using a deep neural network and semantic embeddings.

This paper proposes a cyclic-prefix OFDM system for distributed acoustic sensing (DAS) to eliminate spatial inter-symbol interference and enable shared-waveform integrated sensing and communication.

A spectrogram-based framework is proposed for event detection, localization, and classification in power system waveforms using short-time Fourier transform.

A multimodal Mixture-of-Experts framework was developed for asthma detection using vocal biomarkers and clinical data, achieving better performance than unimodal and bimodal approaches.

The paper proposes a novel, highly secure real-time ECG monitoring framework that uses a patient's own ECG signal characteristics to generate unique, dynamic encryption keys, ensuring confidential dat…

The paper proposes a dual-encoder architecture that fuses processed acoustic waveforms and spectrograms using a differentiable Choquet integral to improve underwater acoustic classification while main…

This paper introduces a curated underwater audio dataset and proposes a margin-enhanced loss with feature alignment for underwater acoustic classification.

A Wave-U-Net model is trained to extract a fundamental waveform from input speech signals for accurate and robust instantaneous pitch estimation.

This paper proposes a framework to protect speaker privacy in dementia detection speech recordings by introducing Cumulative Signal Attack (CSA) at the signal level and Gradient Reversal Layer (GRL) w…

This paper introduces SSTMark, a training-free speech watermarking framework that encodes watermark information into the semantic content of generated speech.

A statistical model is proposed for UWB propagation channels inside the human chest for implant medical sensors, providing formulas for path loss, scattering, and channel impulse response.