A Resilient Novel Compressed-Domain Audio Recognition Method for Anti-Linear Speed Change

2014 ◽  
Vol 620 ◽  
pp. 613-618
Author(s):  
Li Ming Wu ◽  
Wei Han ◽  
Yao Fei Li ◽  
Song Bin Zhou ◽  
Si Cheng Chen
Keyword(s):  
Recognition Rate ◽  
Industrial Process ◽  
Recognition Algorithm ◽  
Practical Significance ◽  
Commercial Application ◽  
Compressed Domain ◽  
Linear Speed ◽  
Time Frequency ◽  
Common Time ◽  
Speed Change

Audio fingerprint technology has been increasingly played an important role in audio content identification, audio information security, industrial process monitoring, etc. Due to compressed format has become the main way for audio files storage and transmission, it owns more practical significance that directly extracting audio fingerprint from compressed-domain. In general, existing compressed-domain audio fingerprint schemes are robust to common time-frequency-domain distortion, including noise, echo, band-pass filtering, 32Kbps@MP3 and others. But they are difficult to deal with large linear speed change distortion which is a frequent audio processing means in the field of television and broadcast. This paper proposes a novel compressed-domain audio recognition algorithm, which can resist linear speed change in the range of-10% to 10% (recognition rate is higher than 90%), via extracting fingerprint after do Fourier-Mellin transform for sub-band energy sequence of MDCT spectrum. This is enough to cope with almost all situations of audio acceleration/deceleration occurred in commercial application. In addition, it shows similarity in other performance compared with existing excellent compressed-domain audio recognition algorithms.

scholarly journals Radiant Source Signal Recognition Algorithm based on SAE

2018 ◽  
Vol 173 ◽  
pp. 02018
Author(s):  
Ye Wen-qiang ◽  
Yu Zhi-fu ◽  
Zhang Kui ◽  
Wang Hu-bang
Keyword(s):  
Recognition Rate ◽  
Training Model ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Signal Recognition ◽  
Softmax Classifier ◽  
Time Frequency ◽  
Reduction Methods ◽  
Source Signal ◽  
Better Than

Aiming at the shortcomings of traditional radar identification based on artificial judgment and module matching, this paper proposes an intelligent identification algorithm based on joint time-frequency. The radar radiation source signal is transformed by time-frequency, and the processed signal is input into the automatic encoder through different kinds of dimensionality reduction methods, and the pre-training adjustment depth learning model is adopted, and the commonly used softmax classifier is adopted to the pre-training model. Oversee fine school and identification, and finally complete the identification task. The simulation results show that high recognition rate can be achieved by this algorithm, and the joint dimension reduction is better than other methods.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Developing the adaptive process-based budgeting through TD-ABC and TD-ABB as part of strategic management accounting

2020 ◽  
Vol 23 (10) ◽  
pp. 1182-1194
Author(s):  
A.A. Akhmetzyanov ◽  
A.Yu. Sokolov
Keyword(s):  
Strategic Management ◽  
Cost Allocation ◽  
Management Accounting ◽  
Practical Significance ◽  
Common Time ◽  
Strategic Management Accounting ◽  
Scientific Papers ◽  
The Subject ◽  
The Common ◽  
The Cost

Subject. The article focuses on the advanced time-driven tools for allocating overhead expenses, which are based on process-based budgeting. Objectives. We articulate a technique for cost allocation so as to assess the cost of each process with reference to the common time driver. Methods. The study relies upon methods of systematization, classification, analogy and comparison, and summarizes the scientific literature on the subject. Results. The article presents our own suggestions on implementing TD-ABC and TD-ABB into the strategic management accounting process of developer companies. The principles were proved to help more effectively allocate overhead expenses and assess the capacity load of each process performed by functions, departments and employees. Carrying out a comparative analysis, we found certain reserves for utilizing resources more effectively. Conclusions and Relevance. The findings are of scientific and practical significance and can be used by developer and construction businesses. The conclusions can prove helpful for scientific papers, student books, and further research.

scholarly journals Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification

2021 ◽  
pp. 1-1
Author(s):  
Yangyang Miao ◽  
Jing Jin ◽  
Ian Daly ◽  
Cili Zuo ◽  
Xingyu Wang ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Time Frequency ◽  
Common Time

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

The Correlation between Time and Frequency Dynamic Performance of Control System

2014 ◽  
Vol 628 ◽  
pp. 186-189
Author(s):  
Meng Xiong Zeng ◽  
Jin Feng Zhao ◽  
Wen Ouyang
Keyword(s):  
Control System ◽  
Performance Index ◽  
Dynamic Performance ◽  
Frequency Domain Analysis ◽  
Practical Significance ◽  
Order System ◽  
Time Frequency ◽  
Performance Indexes ◽  
Quantitative Performance ◽  
The Stability

The control system performance requirement was divided into three parts. They were the stability, rapidity and accuracy. The time-frequency domain analysis in the requirements of three performance were measured through quantitative performance index. The mutual restriction of time-frequency performance and system characteristic parameters of normal second order was discussed. The correlation of system time-frequency performance index was established. The relationship between time-frequency performance indexes in standard two order system was extended to higher order system. The mutually constraining and time-frequency correlation between each performance index was obtained by analysis and calculation. The work had been done above had practical significance to reflect the system dynamic performance in different analytical domains.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

scholarly journals Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhe-Zhou Yu ◽  
Yu-Hao Liu ◽  
Bin Li ◽  
Shu-Chao Pang ◽  
Cheng-Cheng Jia
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Information Needs ◽  
Nonnegative Matrix Factorization ◽  
Recognition Rate ◽  
Nonnegative Matrix ◽  
Recognition Algorithm ◽  
Incremental Method ◽  
Real World Application ◽  
The Face

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

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