Development of Novel Hearing Aids by Using Image Recognition Technology

Lin, Bor-Shing; Liu, Ching-Feng; Cheng, Chih-Jen; Wang, Jhi-Joung; Liu, Chengyu; Li, Jianqing; Lin, Bor‐Shyh

doi:10.1109/jbhi.2018.2836180

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Computer image recognition technology [13][14][15][16][17] is actually the abbreviation of computer image processing and recognition technology, also known as infrared technology. The core of this technology is computers and information.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Image Recognition Technology Based on Machine Learning

2024

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With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Retracted: Image Recognition Technology Based on Machine Learning

2024

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“…Finally, the vertical and horizontal distances of the target could be obtained according to the Pythagorean theorem. The formula derivation and weight relation assignment are discussed in detail in the related research proposed by Lin et al in 2018 [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, there is a lack of discussion on speech processing in real time, especially on how to synchronize video and speech processing. It is necessary to make a breakthrough in this aspect without affecting the immediate speech output [ 2 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach

Liu

Ciou

Chen

et al. 2020

Sensors

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In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.

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Optimized Binaural Enhancement via attention masking network-based speech separation framework in digital hearing aids

Esra,

Sukhi

2024

Computer Speech & Language

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Development of Novel Hearing Aids by Using Image Recognition Technology

Cited by 5 publications

References 23 publications

Retracted: Image Recognition Technology Based on Machine Learning

Retracted: Image Recognition Technology Based on Machine Learning

A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach

Optimized Binaural Enhancement via attention masking network-based speech separation framework in digital hearing aids

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