Smartphone-based single-channel speech enhancement application for hearing aids

2021 ◽  
Vol 150 (3) ◽  
pp. 1663-1673
Author(s):  
Nikhil Shankar ◽  
Gautam Shreedhar Bhat ◽  
Issa M. S. Panahi ◽  
Stephanie Tittle ◽  
Linda M. Thibodeau
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Single Channel

scholarly journals Temporal Auditory Coding Features for Causal Speech Enhancement

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1698
Author(s):  
Iordanis Thoidis ◽  
Lazaros Vrysis ◽  
Dimitrios Markou ◽  
George Papanikolaou
Keyword(s):  
Feature Extraction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Single Channel ◽  
Power Spectra ◽  
Low Frequency ◽  
Audio Signal ◽  
Great Success ◽  
Structure Information ◽  
Feature Extraction And Selection

Perceptually motivated audio signal processing and feature extraction have played a key role in the determination of high-level semantic processes and the development of emerging systems and applications, such as mobile phone telecommunication and hearing aids. In the era of deep learning, speech enhancement methods based on neural networks have seen great success, mainly operating on the log-power spectra. Although these approaches surpass the need for exhaustive feature extraction and selection, it is still unclear whether they target the important sound characteristics related to speech perception. In this study, we propose a novel set of auditory-motivated features for single-channel speech enhancement by fusing temporal envelope and temporal fine structure information in the context of vocoder-like processing. A causal gated recurrent unit (GRU) neural network is employed to recover the low-frequency amplitude modulations of speech. Experimental results indicate that the exploited system achieves considerable gains for normal-hearing and hearing-impaired listeners, in terms of objective intelligibility and quality metrics. The proposed auditory-motivated feature set achieved better objective intelligibility results compared to the conventional log-magnitude spectrogram features, while mixed results were observed for simulated listeners with hearing loss. Finally, we demonstrate that the proposed analysis/synthesis framework provides satisfactory reconstruction accuracy of speech signals.

scholarly journals Speaker-Independent Speech Enhancement with Brain Signals

2021 ◽  
Author(s):  
Maryam Hosseini ◽  
Luca Celotti ◽  
Eric Plourde
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Prior Information ◽  
Single Channel ◽  
Trinity College ◽  
Brain Activity ◽  
Ethics Committees ◽  
Auditory Information ◽  
Trinity College Dublin ◽  
The Brain

Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presence of competing speakers. Recently, it has been shown that the attended auditory information can be decoded from the brain activity of the listener. In this paper, we propose two novel deep learning methods referred to as the Brain Enhanced Speech Denoiser (BESD) and the U-shaped Brain Enhanced Speech Denoiser (U-BESD) respectively, that take advantage of this fact to denoise a multi-talker speech mixture. We use a Feature-wise Linear Modulation (FiLM) between the brain activity and the sound mixture, to better extract the features of the attended speaker to perform speech enhancement. We show, using electroencephalography (EEG) signals recorded from the listener, that U-BESD outperforms a current autoencoder approach in enhancing a speech mixture as well as a speech separation approach that uses brain activity. Moreover, we show that both BESD and U-BESD successfully extract the attended speaker without any prior information about this speaker. This makes both algorithms great candidates for realistic applications where no prior information about the attended speaker is available, such as hearing aids, cellphones, or noise cancelling headphones. All procedures were performed in accordance with the Declaration of Helsinki and were approved by the Ethics Committees of the School of Psychology at Trinity College Dublin, and the Health Sciences Faculty at Trinity College Dublin.

scholarly journals Speaker-Independent Speech Enhancement with Brain Signals

2021 ◽  
Author(s):  
Maryam Hosseini ◽  
Luca Celotti ◽  
Eric Plourde
Keyword(s):  
Speech Enhancement ◽  
Hearing Aids ◽  
Prior Information ◽  
Single Channel ◽  
Trinity College ◽  
Brain Activity ◽  
Ethics Committees ◽  
Auditory Information ◽  
Trinity College Dublin ◽  
The Brain

Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presence of competing speakers. Recently, it has been shown that the attended auditory information can be decoded from the brain activity of the listener. In this paper, we propose two novel deep learning methods referred to as the Brain Enhanced Speech Denoiser (BESD) and the U-shaped Brain Enhanced Speech Denoiser (U-BESD) respectively, that take advantage of this fact to denoise a multi-talker speech mixture. We use a Feature-wise Linear Modulation (FiLM) between the brain activity and the sound mixture, to better extract the features of the attended speaker to perform speech enhancement. We show, using electroencephalography (EEG) signals recorded from the listener, that U-BESD outperforms a current autoencoder approach in enhancing a speech mixture as well as a speech separation approach that uses brain activity. Moreover, we show that both BESD and U-BESD successfully extract the attended speaker without any prior information about this speaker. This makes both algorithms great candidates for realistic applications where no prior information about the attended speaker is available, such as hearing aids, cellphones, or noise cancelling headphones. All procedures were performed in accordance with the Declaration of Helsinki and were approved by the Ethics Committees of the School of Psychology at Trinity College Dublin, and the Health Sciences Faculty at Trinity College Dublin.

Sign in / Sign up

Export Citation Format

Share Document