Search Results for author:
Found 11 papers, 2 papers with code
How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses
Our results show that this new optimization objective significantly improves the emulation performance of deep neural networks across relevant input sound levels and auditory-model frequency channels, without increasing the computational load during inference.
Neural Networks Hear You Loud And Clear: Hearing Loss Compensation Using Deep Neural Networks
In this study, we propose a DNN-based approach for hearing-loss compensation, which is trained on the outputs of hearing-impaired and normal-hearing DNN-based auditory models in response to speech signals.
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
Our experiments show that self-supervised pretraining not only improves performance in clean conditions, but also yields models which are more robust to adverse conditions compared to purely supervised learning.
Investigating the Design Space of Diffusion Models for Speech Enhancement
To address this, we extend this framework to account for the progressive transformation between the clean and noisy speech signals.
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
We show that the proposed system substantially benefits from using multiple databases for training, and achieves superior performance compared to state-of-the-art discriminative models in both matched and mismatched conditions.
Head Orientation Estimation with Distributed Microphones Using Speech Radiation Patterns
Determining the head orientation of a talker is not only beneficial for various speech signal processing applications, such as source localization or speech enhancement, but also facilitates intuitive voice control and interaction with smart environments or modern car assistants.
Joint Minimum Processing Beamforming and Near-end Listening Enhancement
Results show that the joint optimization can further improve performance compared to the concatenated approach.
Distributed Adaptive Norm Estimation for Blind System Identification in Wireless Sensor Networks
Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i. e., fusion center) for processing.
Minimum Processing Near-end Listening Enhancement
The intelligibility and quality of speech from a mobile phone or public announcement system are often affected by background noise in the listening environment.
Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index
However, the existing optimal mutual information based method requires a complicated system model that includes natural speech variations, and relies on approximations and assumptions of the underlying signal distributions.
Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset
Moreover, the paper advances on a recent deep learning-based method for sound field reconstruction using a very low number of microphones, and proposes an approach for modeling both magnitude and phase response in a U-Net-like neural network architecture.
