Autonomic Security and Self-Protection based on Feature-Recognition with Virtual Neurons

Promoting Hearing Loss Prevention in Audiology Practice

Perspectives on Public Health Issues Related to Hearing and Balance ◽

10.1044/hcoa13.1.3 ◽

2012 ◽

Vol 13 (1) ◽

pp. 3-19 ◽

Cited By ~ 5

Author(s):

David C. Byrne ◽

Christa L. Themann ◽

Deanna K. Meinke ◽

Thais C. Morata ◽

Mark R. Stephenson

Keyword(s):

Hearing Loss ◽

Sound Level ◽

Health Campaigns ◽

Loss Prevention ◽

Hearing Conservation ◽

Prevention Services ◽

Occupational Programs ◽

Strategic Position ◽

Public Health Campaigns ◽

Self Protection

An audiologist should be the principal provider and advocate for all hearing loss prevention activities. Many audiologists equate hearing loss prevention with industrial audiology and occupational hearing conservation programs. However, an audiologist’s involvement in hearing loss prevention should not be confined to that one particular practice setting. In addition to supervising occupational programs, audiologists are uniquely qualified to raise awareness of hearing risks, organize public health campaigns, promote healthy hearing, implement intervention programs, and monitor outcomes. For example, clinical audiologists can show clients how to use inexpensive sound level meters, noise dosimeters, or phone apps to measure noise levels, and recommend appropriate hearing protection. Audiologists should identify community events that may involve hazardous exposures and propose strategies to minimize risks to hearing. Audiologists can help shape the knowledge, beliefs, motivations, attitudes, and behaviors of individuals toward self-protection. An audiologist has the education, tools, opportunity, and strategic position to facilitate or promote hearing loss surveillance and prevention services and activities. This article highlights real-world examples of the various roles and substantial contributions audiologists can make toward hearing loss prevention goals.

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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Recognition of classroom student state features based on deep learning algorithms and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189232 ◽

2020 ◽

pp. 1-12

Author(s):

Hu Jingchao ◽

Haiying Zhang

Keyword(s):

Deep Learning ◽

Feature Recognition ◽

Subjective Evaluation ◽

Recognition Algorithm ◽

Video Data ◽

State Recognition ◽

Detection Model ◽

State Classification ◽

Intelligent Models ◽

Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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