Enhanced smart hearing aid using deep neural networks

Nossier, Soha A.; Rizk, Mohamed R. M.; Moussa, Nancy Diaa; Shehaby, Saleh el

doi:10.1016/j.aej.2019.05.006

Cited by 26 publications

(14 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the number of electrodes and which electrodes are required to achieve acceptable performance should be determined (Mirkovic et al, 2015;Montoya-Martínez, Bertrand & Francart, 2019;Narayanan & Bertrand, 2018). In most of the studies, the analysis is carried out with ordinary machine learning algorithms, and a few studies are investigated with the deep learning approaches (Krizhevsky, Sutskever & Hinton, 2017;Nossier et al, 2019;Shao et al, 2019). However, most of the studies' testing accuracy is not enough to use the model in real-time as well as real-life applications.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, selecting a short decision window makes the system faster by reducing the computational complexity of the system. On the other hand, Deep learning (DL) approaches can provide an effective solution because of their effective feature learning capability to overcome the above limitations (Krizhevsky, Sutskever & Hinton, 2017;Nossier et al, 2019;Shao et al, 2019;Bari et al, 2021;Mahendra Kumar et al, 2021). Deep learning models have several hidden layers that can explicitly learn hierarchical representations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Islam

Sulaiman

Farid

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Islam

Sulaiman

Farid

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…A smart HAD employing a deep neural network is used to enhance three specific sounds namely a fire alarm, a car horn, and a baby cry is presented in [ 24 ]. This is a significant contribution to people suffering from hearing loss to avoid catastrophic occurrences.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Siddiqui

Saleem

Raza

et al. 2022

Sensors

View full text Add to dashboard Cite

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene.

show abstract

“…Three different sound engine classifiers were tested: K-Nearest Neighbor (KNN), Neural Network (NN), and Decision Tree (DT); a three-layer NN with 60 nodes achieved the best results. Furthermore, a smart hearing aid detects and makes audible important noises (e.g., fire alarm, car horn) [30]. The designed deep neural network can treat input noise differently according to its classification (important, or not important).…”

Section: Sound Recognition Systemsmentioning

confidence: 99%

An Overview of Machine Learning and 5G for People with Disabilities

Domingo¹

2021

Sensors

View full text Add to dashboard Cite

Currently, over a billion people, including children (or about 15% of the world’s population), are estimated to be living with disability, and this figure is going to increase to beyond two billion by 2050. People with disabilities generally experience poorer levels of health, fewer achievements in education, fewer economic opportunities, and higher rates of poverty. Artificial intelligence and 5G can make major contributions towards the assistance of people with disabilities, so they can achieve a good quality of life. In this paper, an overview of machine learning and 5G for people with disabilities is provided. For this purpose, the proposed 5G network slicing architecture for disabled people is introduced. Different application scenarios and their main benefits are considered to illustrate the interaction of machine learning and 5G. Critical challenges have been identified and addressed.

show abstract

Enhanced smart hearing aid using deep neural networks

Cited by 26 publications

References 20 publications

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

An Overview of Machine Learning and 5G for People with Disabilities

Contact Info

Product

Resources

About