scholarly journals Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hong Kai
Keyword(s):  
Internet Of Things ◽  
Feature Recognition ◽  
Recognition System ◽  
Feature Analysis ◽  
Application Layer ◽  
Regularization Algorithm ◽  
Music Signal ◽  
Dynamic Time ◽  
Music Signals ◽  
Maximum Similarity

Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion.

scholarly journals Cloud Computing Image Recognition System Assists the Construction of the Internet of Things Model of Administrative Management Event Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peikun Xie ◽  
Enchen Ma ◽  
Zaihua Xu
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Spatial Structure ◽  
Image Recognition ◽  
Recognition System ◽  
The Internet ◽  
Fusion Method ◽  
Application Layer ◽  
Administrative Management ◽  
The Internet Of Things

In order to successfully apply the Internet of Things and cloud computing to the administrative management of spatial structures and realize the systematization, digitization, and intelligence of administrative management, this article draws on research experience in related fields and considers the data characteristics and computing tasks of administrative management. The whole cycle of transmission, storage, postprocessing, and visualization is the main line of research, and a cloud computing-based spatial structure administrative management IoT system is constructed. First, by summarizing the application status of the Internet of Things, the general Internet of Things system is summarized into three levels, and combined with the specific work in the spatial structure administrative management, the overall framework of the spatial structure administrative management of the Internet of Things system is proposed, and the functional sublayers are carried out. Secondly, in response to the above problems, through the traditional image recognition system research and practical application investigation, in order to meet the user’s requirements for the computing efficiency and recognition accuracy of the image recognition system, an image recognition system in the cloud computing environment is proposed. It proposes a fuzzy evaluation algorithm of health grade hierarchy analysis optimized for the index system and scoring system and a calculation method that uses time series to identify regular outliers. The optical image pixel-level fusion method and the infrared and visible image fusion method based on complementary information are proposed, and the image fusion software is developed. Finally, in order to enable the application layer to use cluster resources to efficiently and intelligently process massive monitoring data containing redundancy, heterogeneity, anomalies, and many other defects, according to the calculation process of each specific task of data preprocessing and postprocessing in the application layer, demonstrations are made one by one. After analysis, it is concluded that vertical storage of data blocks according to different sensor channels is the optimal strategy.

scholarly journals Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Jiang ◽  
Dong Sun
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Classification Model ◽  
Inversion Method ◽  
Temporal Segmentation ◽  
Second Stage ◽  
Music Signal ◽  
Music Score ◽  
Stage Classification ◽  
Music Signals

Digitization and analysis processing technology of music signals is the core of digital music technology. The paper studies the music signal feature recognition technology based on the mathematical equation inversion method, which is aimed at designing a method that can help music learners in music learning and music composition. The paper firstly studies the modeling of music signal and its analysis and processing algorithm, combining the four elements of music sound, analyzing and extracting the characteristic parameters of notes, and establishing the mathematical model of single note signal and music score signal. The single note recognition algorithm is studied to extract the Mel frequency cepstrum coefficient of the signal and improve the DTW algorithm to achieve single note recognition. Based on the implementation of the single note algorithm, we combine the note temporal segmentation method based on the energy-entropy ratio to segment the music score into single note sequences to realize the music score recognition. The paper then goes on to study the music synthesis algorithm and perform simulations. The benchmark model demonstrates the positive correlation of pitch features on recognition through comparative experiments and explores the number of harmonics that should be attended to when recognizing different instruments. The attention network-based classification model draws on the properties of human auditory attention to improve the recognition scores of the main playing instruments and the overall recognition accuracy of all instruments. The two-stage classification model is divided into a first-stage classification model and a second-stage classification model, and the second-stage classification model consists of three residual networks, which are trained separately to specifically identify strings, winds, and percussions. This method has the highest recognition score and overall accuracy.

Reconfigurable Feature Recognition for an Adaptable, Maintainable CAD/CAPP Integration

1996 ◽  
Author(s):  
Daniel M. Gaines ◽  
Fernando Castaño ◽  
Caroline C. Hayes
Keyword(s):  
Feature Recognition ◽  
Recognition System ◽  
Manufacturing Processes ◽  
Manufacturing Equipment ◽  
Recognition Systems

Abstract This paper presents MEDIATOR, a feature recognition system which is designed to be maintainable and extensible to families of related manufacturing processes. A problem in many feature recognition systems is that they are difficult to maintain. One of the reasons may be because they depend on use of a library of feature-types which are difficult to update when the manufacturing processes change due to changes in the manufacturing equipment. The approach taken by MEDIATOR is based on the idea that the properties of the manufacturing equipment are what enable manufacturable shapes to be produced in a part. MEDIATOR’S method for identifying features uses a description of the manufacturing equipment to simultaneously identify manufacturable volumes (i.e. features) and methods for manufacturing those volumes. Maintenance of the system is simplified because only the description of the equipment needs to be updated in order to update the features identified by the system.

scholarly journals Navigation Security Module with Real-Time Voice Command Recognition System

2017 ◽  
Vol 24 (2) ◽  
pp. 17-26
Author(s):  
Mustafa Yagimli ◽  
Huseyin Kursat Tezer
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Coding ◽  
Recognition System ◽  
Time Warping ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Command ◽  
Security Module ◽  
Dynamic Time

Abstract The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters.

scholarly journals Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiujin Yu ◽  
Shengfu Liu ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Recognition ◽  
High Efficiency ◽  
Chinese Language ◽  
Strong Support ◽  
Superior Performance ◽  
Feature Analysis ◽  
Data Mining Techniques ◽  
Self Organizing

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

scholarly journals Introduction to the Special Issue: Applications of Internet of Things

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 374 ◽  
Author(s):  
Chi-Hua Chen ◽  
Eyhab Al-Masri ◽  
Feng-Jang Hwang ◽  
Despo Ktoridou ◽  
Kuen-Rong Lo
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Data Science ◽  
Cooperative Spectrum Sensing ◽  
Smart Homes ◽  
Recognition System ◽  
Special Issue ◽  
Smart Transportation ◽  
User Classification ◽  
Novel Approach

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.

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