Modeling of a multiple digital automatic gain control system

Wang, Jingdian; Lu, Xiuhong; Zhang, Li

doi:10.1016/s1007-0214(08)72204-1

Cited by 8 publications

(2 citation statements)

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“…The specific method is to find out the difference of frame features according to the time sequence, and then divide the difference of phonological features by the total difference of phonological features to get the relative accumulated difference of features. In this way, although the pronunciation speed is different, the relative cumulative difference of phonological features is basically unchanged [7]. Through the analysis of speech data, it is found that although the spectrum of the end segment changes dramatically, the semantics of the end segment are few, and it has little effect on distinguishing speech.…”

Section: Related Workmentioning

confidence: 97%

MCU-Based Isolated Appealing Words Detecting Method with AI Techniques

Liang

Dong

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Bullying in campus has attracted more and more attention in recent years. By analyzing typical campus bullying events, it can be found that the victims often use the words "help" and some other appealing or begging words, that is to say, by using the artificial intelligence of speech recognition, we can find the occurrence of campus bullying events in time, and take measures to avoid further harm. The main purpose of this study is to help the guardians discover the occurrence of campus bullying in time by real-time monitoring of the keywords of campus bullying, and take corresponding measures in the first time to minimize the harm of campus bullying. On the basis of Sunplus MCU and speech recognition technology, by using the MFCC acoustic features and an efficient DTW classifier, we were able to realize the detection of common vocabulary of campus bullying for the specific human voice. After repeated experiments, and finally combining the voice signal processing functions of Sunplus MCU, the recognition procedure of specific isolated words was completed. On the basis of realizing the isolated word detection of specific human voice, we got an average accuracy of 99% of appealing words for the dedicated speaker and the misrecognition rate of other words and other speakers was very low.

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Section: Related Workmentioning

confidence: 97%

MCU-Based Isolated Appealing Words Detecting Method with AI Techniques

Liang

Dong

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…This non-linearity, combined with radio channel time variance, complicates both analysis and modeling [11][12] [14]. Therefore, much of the published AGC research work tends to be empirical in the literature [17].…”

Section: IImentioning

confidence: 99%

AGC-based RF Fingerprints in Wireless Sensor Networks for authentication

Knox

Kunz

2010

2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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RF Fingerprints can be used to distinguish between distinct transmitter signals in a Wireless Sensor Network. We present implementation results for RF Fingerprints based on Automatic Gain Control circuitry, which is present on existing IEEE 802.15.4 WSN node hardware and in other radio systems. We analyze implementation constraints and their different effects on our results. Different noise sources affect RF Fingerprint measurement accuracy on a real hardware platform. We present a simple method to improve both accuracy and reliability.

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