scholarly journals A Signal Period Detection Algorithm Based on Morphological Self-Complementary Top-Hat Transform and AMDF

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Zhao Han ◽  
Xiaoli Wang
Keyword(s):  
Detection Algorithm ◽  
Periodic Signal ◽  
Detection Accuracy ◽  
Speech Signal Processing ◽  
Difference Function ◽  
Detection Technology ◽  
Period Detection ◽  
Magnitude Difference ◽  
Better Than ◽  
Average Magnitude Difference Function

Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics.

Average magnitude difference function pitch extractor

1974 ◽  
Vol 22 (5) ◽  
pp. 353-362 ◽  
Author(s):  
M. Ross ◽  
H. Shaffer ◽  
A. Cohen ◽  
R. Freudberg ◽  
H. Manley
Keyword(s):  
Difference Function ◽  
Magnitude Difference ◽  
Average Magnitude Difference Function

scholarly journals Pitch Estimation using Time Domain Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 1391-1394
Keyword(s):  
Time Domain ◽  
Vocal Folds ◽  
Detection Algorithm ◽  
Pitch Detection ◽  
Pitch Estimation ◽  
The Time Domain ◽  
Short Time ◽  
The Voice ◽  
Magnitude Difference ◽  
Average Magnitude Difference Function

Speech is classified into voice, unvoiced and silence. The voice speech is the periodic vibration of vocal folds. Background noise affects the speech signals. In many speech applications calculation of pitch plays a major role. The paper proposes a pitch detection algorithm based on the short-time average magnitude difference function (AMDF) and the short-term autocorrelation function (ACF). Detecting the Pitch within the speech signal is important in most of all the speech related applications. Detection of Pitch is useful in identification of speaker. One solution to get detect with the pitch is by using the time domain algorithms. This paper gives idea about estimation and detection of pitch in time domain algorithm for different voice samples

Research and Achievement on Cigarette Label Printing Defect Detection Algorithm

2012 ◽  
Vol 200 ◽  
pp. 689-693
Author(s):  
Meng Xiao Li ◽  
Quan Xiang Liu ◽  
Cong You He
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Analytical Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Defect Analysis ◽  
Image Sensing ◽  
Detection Technology ◽  
Image Difference

The cigarette label printing defect detection algorithm mainly includes image sensing and defect analysis. This paper carried out a deep research and analysis on the cigarette label printing defect detection algorithm by combing the domestic and foreign advanced machine vision-based printing defect detection technology, and improved the existing defect detection algorithm. Besides, it proposed to apply the minimum external rectangle in analyzing defect shape, realized the functions of detecting defect of printing images as well as analyzing and displaying the defect, and proved that the combination application of image difference and minimum external rectangle analytical method can bring better timeliness and higher detection accuracy.

scholarly journals Towards Pedestrian Target Detection with Optimized Mask R-CNN

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Dong-Hao Chen ◽  
Yu-Dong Cao ◽  
Jia Yan
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Width Ratio ◽  
Detection Accuracy ◽  
Natural Scene ◽  
Original Algorithm ◽  
Detection Algorithms ◽  
Length Width ◽  
Natural Characteristics ◽  
Better Than

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.

scholarly journals Lightweight Fruit-Detection Algorithm for Edge Computing Applications

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenli Zhang ◽  
Yuxin Liu ◽  
Kaizhen Chen ◽  
Huibin Li ◽  
Yulin Duan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Sampling Method ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Model Structure ◽  
Excellent Performance ◽  
Backbone Network ◽  
Detection Technology

In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck and hindering the utilization of AI algorithms in modern horticulture. In this paper, we propose a lightweight fruit-detection algorithm, specifically designed for edge devices. The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices while maintaining the fruit-detection accuracy. The proposed algorithm was tested on three edge devices: NVIDIA Jetson Xavier NX, NVIDIA Jetson TX2, and NVIDIA Jetson NANO. The experimental results show that the average detection precision of the proposed algorithm for orange, tomato, and apple datasets are 0.93, 0.847, and 0.850, respectively. Deploying the algorithm, the detection speed of NVIDIA Jetson Xavier NX reaches 21.3, 24.8, and 22.2 FPS, while that of NVIDIA Jetson TX2 reaches 13.9, 14.1, and 14.5 FPS and that of NVIDIA Jetson NANO reaches 6.3, 5.0, and 8.5 FPS for the three datasets. Additionally, the proposed algorithm provides a component add/remove function to flexibly adjust the model structure, considering the trade-off between the detection accuracy and speed in practical usage.

Driver Voice Identification System Using Auto-Correlation Function and Average Magnitude Difference Function

2014 ◽  
Vol 490-491 ◽  
pp. 1287-1292 ◽  
Author(s):  
Jian Da Wu ◽  
Pang Yi Liu ◽  
Guan Long Hong
Keyword(s):  
Correlation Function ◽  
Identification System ◽  
Zero Crossing ◽  
Voice Identification ◽  
Auto Correlation ◽  
Difference Function ◽  
Auto Correlation Function ◽  
The Voice ◽  
Magnitude Difference ◽  
Average Magnitude Difference Function

This study presents a driver identification system using voice analysis for a vehicle security system. The structure of the proposed system has three parts. The first procedure is speech pre-processing, the second is feature extraction of sound signals, and the third is classification of driver voice. Initially, a database of sound signals for several drivers was established. The volume and zero-crossing rate (ZCR) of sound are used to detect the voice end-point in order to reduce data computation. Then the Auto-correlation Function (ACF) and Average Magnitude Difference Function (AMDF) methods are applied to retrieve the voice pitch features. Finally these features are used to identify the drivers by a General Regression Neural Network (GRNN). The experimental results show that the development of this voice identification system can use fewer feature vectors of pitch to obtain a good recognition rate.

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