Hearing, listening and deep neural networks in hearing aids

2021 ◽  
Vol 13 (1) ◽  
pp. 5-8
Author(s):  
Douglas L Beck
Keyword(s):  
Neural Networks ◽  
Hearing Aids ◽  
Digital Technology ◽  
Deep Neural Networks ◽  
Sound Processing ◽  
Speech In Noise ◽  
Acoustic Feedback ◽  
Processing Techniques ◽  
High Sound ◽  
Very High

Hearing aids have undergone vast changes in the last 30 years from basic analog sound processing techniques, to advanced digital technology, to Deep Neural Networks (DNNs) “on-the-chip” providing real-time sound processing. In addition to making sounds audible, advanced hearing aids with DNN on-the-chip are better able to provide clearer understanding of speech in noise, improve recall, maintain interaural loudness and timing differences, and improve the wearer’s ability to selectively attend to the speaker of choice in challenging listening situations. These improvements are delivered without acoustic feedback and with very high sound quality.

scholarly journals Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises

2020 ◽  
Vol 10 (17) ◽  
pp. 6077
Author(s):  
Gyuseok Park ◽  
Woohyeong Cho ◽  
Kyu-Sung Kim ◽  
Sangmin Lee
Keyword(s):  
Neural Networks ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Deep Neural Networks ◽  
Hearing Aid ◽  
Environmental Noise ◽  
Speech Quality ◽  
Impaired Hearing ◽  
Noise Classification

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

scholarly journals STUDY ON BRAIN TUMOR CLASSIFICATION THROUGH MRI IMAGES USING A DEEP CONVOLUTIONAL NEURAL NETWORK

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Brain Tumor ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Normal Organ ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Processing Techniques ◽  
Mri Image

Brain tumor (Glioma) is one of the deadliest diseases that attack humans, now even men or women aged 20-30 are suffering from this disease. To cure tumor in a person, doctors use MRI machine, because the results of MRI images are proven to provide better image results than CT-Scan images, but sometimes it is difficult to distinguish between the MRI images having tumors with that images not having tumor from MRI image results. It is because of resulting contrast is like any other normal organ. However, using features of image processing techniques like scaling, contrast enhancement and thresh-holding based in Deep Neural Networks the scheme can classify the results more appropriately and with high accuracy. In this paper, this study reveals the nitty-gritty of Brain tumor (Gliomas) and Deep Learning techniques for better inception in the field of computer-vision.

scholarly journals Exposing Digital Image Forgeries by Detecting Contextual Abnormality Using Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2262
Author(s):  
Haneol Jang ◽  
Jong-Uk Hou
Keyword(s):  
Neural Networks ◽  
Digital Image ◽  
Deep Neural Networks ◽  
Image Forensics ◽  
Spatial Context ◽  
Digital Image Forensics ◽  
Image Manipulation ◽  
Image Processing Techniques ◽  
Low Level ◽  
Processing Techniques

Traditionally, digital image forensics mainly focused on the low-level features of an image, such as edges and texture, because these features include traces of the image’s modification history. However, previous methods that employed low-level features are highly vulnerable, even to frequently used image processing techniques such as JPEG and resizing, because these techniques add noise to the low-level features. In this paper, we propose a framework that uses deep neural networks to detect image manipulation based on contextual abnormality. The proposed method first detects the class and location of objects using a well-known object detector such as a region-based convolutional neural network (R-CNN) and evaluates the contextual scores according to the combination of objects, the spatial context of objects and the position of objects. Thus, the proposed forensics can detect image forgery based on contextual abnormality as long as the object can be identified even if noise is applied to the image, contrary to methods that employ low-level features, which are vulnerable to noise. Our experiments showed that our method is able to effectively detect contextual abnormality in an image.

scholarly journals Bird Detection using Siamese Neural Network

2020 ◽  
Vol 9 (7) ◽  
pp. 1168-1171
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Bird Species ◽  
Siamese Network ◽  
The Neural Network ◽  
Data Points ◽  
Bird Detection ◽  
Processing Techniques ◽  
Do So

The proposed system uses deep neural networks for identifying bird species. The model will be trained on bird images that are coming in the endangered species category. The application can also handle new data points, unlike existing systems that require model re-training for accommodating new data. The system can identify bird species in a large view of the image. The model will be trained using a convolutional neural network-based architecture called Siamese Network. This network is also called one-shot learning which means that it requires only few training example for each class. Existing models use image processing techniques or vanilla convolutional neural networks for classifying bird images. These models cannot accommodate new images and have to be retrained to do so. There is no commercially available system that can detect a species of bird in high resolution / large image. While in the Siamese network we only have to add new data, there is no need to retraining the neural network.

scholarly journals Hearing Aid Selection: Closed- versus Open-canal Fitting Hearing Aids

2021 ◽  
Vol 17 (4) ◽  
pp. 323-330
Author(s):  
Sangyeon Lee ◽  
Soo Hee Oh ◽  
Kyoungwon Lee
Keyword(s):  
Hearing Aids ◽  
Hearing Aid ◽  
Rechargeable Batteries ◽  
Hearing Rehabilitation ◽  
Directional Microphones ◽  
Speech In Noise ◽  
Acoustic Feedback ◽  
Hearing Thresholds ◽  
Occlusion Effect ◽  
Selection Of

To select hearing aid is an essential process for successful hearing rehabilitation. The purpose of this study is to review hearing aid selection considerations between receiver in-the-canal (RIC) and custom hearing aid (CHA) in order to guide appropriate selection of the hearing aid. This study discussed three key factors in the hearing aid selection including physical, acoustic and electroacoustic characteristics and other aspects. Advantages of RIC types are comfort to wear, reduction of the occlusion effect, presence of directional microphones, on-site fit, easy connectivity with other devices, and use of rechargeable batteries. On the other hand, the CHA types have their advantage in terms of being comfort to wear with masks, proper insertion and placement, reduction of the acoustic feedback, good approximation of frequency response curve, improvement of speech in noise perception, expanded hearing aid candidacy with varying hearing thresholds, and easy telephone use. We concluded that appropriate selection of the hearing aid would contribute to successful hearing rehabilitation, if considering physical, psycho-social, and acoustical characteristics.

scholarly journals Accelerating approximate aggregation queries with expensive predicates

2021 ◽  
Vol 14 (11) ◽  
pp. 2341-2354
Author(s):  
Daniel Kang ◽  
John Guibas ◽  
Peter Bailis ◽  
Tatsunori Hashimoto ◽  
Yi Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Query Processing ◽  
Deep Neural Networks ◽  
Optimal Allocation ◽  
Approximate Query Processing ◽  
Approximate Aggregation ◽  
Approximate Query ◽  
Real World Datasets ◽  
Aggregation Queries ◽  
Processing Techniques

Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using expensive deep neural networks (DNNs). As these DNNs are expensive and because many applications can tolerate approximate answers, analysts are interested in accelerating these queries via approximations. Unfortunately, standard approximate query processing techniques to accelerate such queries are not applicable because they assume the result of the predicates are available ahead of time. Furthermore, recent work using cheap approximations (i.e., proxies) do not support aggregation queries with predicates. To accelerate aggregation queries with expensive predicates, we develop and analyze a query processing algorithm that leverages proxies (ABAE). ABAE must account for the key challenge that it may sample records that do not satisfy the predicate. To address this challenge, we first use the proxy to group records into strata so that records satisfying the predicate are ideally grouped into few strata. Given these strata, ABAE uses pilot sampling and plugin estimates to sample according to the optimal allocation. We show that ABAE converges at an optimal rate in a novel analysis of stratified sampling with draws that may not satisfy the predicate. We further show that ABAE outperforms on baselines on six real-world datasets, reducing labeling costs by up to 2.3X.

Equivalent Noise Dose Obtained Through Hearing Aids in the Classrooms of Hearing-Impaired Children

1990 ◽  
Vol 21 (3) ◽  
pp. 147-150
Author(s):  
Ronald A. Wilde
Keyword(s):  
Hearing Aids ◽  
High Gain ◽  
Hearing Impaired ◽  
Deaf Children ◽  
Impaired Children ◽  
Very High ◽  
Hearing Impaired Children

A commercial noise dose meter was used to estimate the equivalent noise dose received through high-gain hearing aids worn in a school for deaf children. There were no significant differences among nominal SSPL settings and all SSPL settings produced very high equivalent noise doses, although these are within the parameters of previous projections.

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