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Found 27 papers, 6 papers with code
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.
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.
Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks
Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial awareness.
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement
Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years.
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.
Joint Minimum Processing Beamforming and Near-end Listening Enhancement
Results show that the joint optimization can further improve performance compared to the concatenated approach.
Speech inpainting: Context-based speech synthesis guided by video
Audio and visual modalities are inherently connected in speech signals: lip movements and facial expressions are correlated with speech sounds.
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.
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting
In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance.
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.
Deep Spoken Keyword Spotting: An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
Audio-Visual Speech Inpainting with Deep Learning
In this paper, we present a deep-learning-based framework for audio-visual speech inpainting, i. e., the task of restoring the missing parts of an acoustic speech signal from reliable audio context and uncorrupted visual information.
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.
Exploring Filterbank Learning for Keyword Spotting
Despite their great performance over the years, handcrafted speech features are not necessarily optimal for any particular speech application.
Vocoder-Based Speech Synthesis from Silent Videos
Both acoustic and visual information influence human perception of speech.
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement
Finally, we show that a loss function based on scale-invariant signal-to-distortion ratio (SI-SDR) achieves good general performance across a range of popular speech enhancement evaluation metrics, which suggests that SI-SDR is a good candidate as a general-purpose loss function for speech enhancement systems.
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers
Our results show that this multi-task deep residual network is able to achieve a KWS accuracy relative improvement of around 32% with respect to a system that does not deal with external speakers.
Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
Regarding speech intelligibility, we find a general tendency of the benefit in training the systems with Lombard speech.
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker.
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems
Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect.
Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure
Finally, we show that the proposed SE system performs on par with a traditional DNN based Short-Time Spectral Amplitude (STSA) SE system in terms of estimated speech intelligibility.
Sound Audio and Speech Processing
Joint Separation and Denoising of Noisy Multi-talker Speech using Recurrent Neural Networks and Permutation Invariant Training
We show that deep bi-directional LSTM RNNs trained using uPIT in noisy environments can improve the Signal-to-Distortion Ratio (SDR) as well as the Extended Short-Time Objective Intelligibility (ESTOI) measure, on the speaker independent multi-talker speech separation and denoising task, for various noise types and Signal-to-Noise Ratios (SNRs).
Sound
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on Non-negative Matrix Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network (DANet).
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
We propose a novel deep learning model, which supports permutation invariant training (PIT), for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem.
