scholarly journals Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Daliang Wang ◽  
Xiaowen Guo
Keyword(s):  
Complex System ◽  
Classification Accuracy ◽  
Music Performance ◽  
Classification Algorithm ◽  
Audio Signals ◽  
Fusion Algorithm ◽  
Before And After ◽  
Intelligent Recognition ◽  
Better Than

In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.

scholarly journals Research on Music Emotion Intelligent Recognition and Classification Algorithm in Music Performance System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chun Huang ◽  
Diao Shen
Keyword(s):  
Feature Vector ◽  
Music Performance ◽  
Classification Algorithm ◽  
Classification Model ◽  
Audio Features ◽  
The Neural Network ◽  
Training Samples ◽  
Performance System ◽  
Intelligent Recognition

The music performance system works by identifying the emotional elements of music to control the lighting changes. However, if there is a recognition error, a good stage effect will not be able to create. Therefore, this paper proposes an intelligent music emotion recognition and classification algorithm in the music performance system. The first part of the algorithm is to analyze the emotional features of music, including acoustic features, melody features, and audio features. Then, the three kinds of features are combined together to form a feature vector set. In the latter part of the algorithm, it divides the feature vector set into training samples and test samples. The training samples are trained by using recognition and classification model based on the neural network. And then, the testing samples are input into the trained model, which is aiming to realize the intelligent recognition and classification of music emotion. The result shows that the kappa coefficient k values calculated by the proposed algorithm are greater than 0.75, which indicates that the recognition and classification results are consistent with the actual results, and the accuracy of recognition and classification is high. So, the research purpose is achieved.

scholarly journals Classification of Unbalanced Data Based on RSM and Binomial Distribution

2020 ◽  
Author(s):  
Rong Li ◽  
Wei-Bai Zhou
Keyword(s):  
Dimension Reduction ◽  
Binomial Distribution ◽  
Classification Accuracy ◽  
Classification Algorithm ◽  
Unbalanced Data ◽  
Minority Class ◽  
Model Interpretation ◽  
High Classification Accuracy ◽  
Random Part

In the case of extremely unbalanced data, the results of the traditional classification algorithm are very unbalanced, and most samples are often divided into the categories of majority samples, so the accuracy of judgment of the minority classes will be reduced. In this paper, we propose a classification algorithm for unbalanced data based on RSM and binomial undersampling. We use RSM’s random part features rather than all each classifier to make each training classifier reduce the dimensions, and dimension reduction makes relatively minority class samples indirectly lift. Using the above characteristics of the RSM to reduce dimension can solve the problem that unbalanced data classification in the minority class samples is too little, and it can also find the important attribute of variables to make the model have the ability of explanation. Experiments show that our algorithm has high classification accuracy and model interpretation ability when classifying unbalanced data.

Categorical Effects in the Perception of Familiar Faces

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 71-71
Author(s):  
A Angeli ◽  
W Gerbino
Keyword(s):  
Classification Accuracy ◽  
Control Experiment ◽  
Categorical Perception ◽  
Discrimination Accuracy ◽  
Sequential Classification ◽  
Better Than ◽  
The Continuum

Photographs of morphed faces were shown to close friends of portrayed individuals. Three tasks were used: localisation of a morphed target on the continuum between the two original faces, simultaneous same - different discrimination of face pairs separated by a 20% morphing step (AB task), and sequential classification of the same pairs (ABX task). Localisation data were plotted against morph coefficients. Evidence of categorical processing was provided by steeper functions for upright vs upside-down faces. In the AB task, intermediate faces were discriminated better than faces separated by the same morphing step but closer to one original. This was confirmed in a control experiment where the participants were unfamiliar with portrayed individuals and were unlikely to process our stimuli categorically. The superiority of intermediate faces in the AB task was attributed to a nonlinearity of continua generated by the morphing procedure, and used as a baseline to evaluate ABX classification data. Also in the ABX task, intermediate faces, those straddling the categorical boundary, were classified more accurately than faces located on the same side of the boundary. However, the superiority in classification accuracy was larger than the superiority in discrimination accuracy operationalised by the AB task, as predicted by the categorical perception hypothesis.

Performance Comparison of Methods for Tree Species Classification in Multispectral Images

1970 ◽  
Vol 111 (5) ◽  
pp. 119-122 ◽  
Author(s):  
R. Dinuls ◽  
A. Lorencs ◽  
I. Mednieks
Keyword(s):  
Classification Accuracy ◽  
Field Work ◽  
Performance Comparison ◽  
Multispectral Images ◽  
Species Classification ◽  
Multispectral Data ◽  
Individual Trees ◽  
Favorable Conditions ◽  
Better Than

A number of methods for classification of individual trees in high resolution multispectral images have been developed. The paper provides comparative analysis of some practicable methods of such type. Classification accuracy into 5 species was tested by computer simulations with real multispectral data obtained using airborne hyperspectral sensor. Coordinates and species of individual trees were supplied for testing by field work. It is shown that classification accuracy better than 97 % can be reached by more sophisticated methods in favorable conditions. Presented results can be used to choose a classification method appropriate for the particular forest inventory task. Ill. 1, bibl. 7 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.371

The Research of LandCover Classification on Optical Image and Radar Image

2012 ◽  
Vol 500 ◽  
pp. 330-334
Author(s):  
Yin Xuan Cao ◽  
Zheng Zhao
Keyword(s):  
High Resolution ◽  
Classification Accuracy ◽  
Radar Image ◽  
Hengduan Mountains ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Spectral Image ◽  
Visual Effect ◽  
Landcover Classification ◽  
Better Than

This paper performed the fusion test using high resolution TerraSAR and ALOS optical multi-spectral image in Hengduan mountains area. The results of automatic classification compared to the visual effect for fusion image indicated that the classification accuracy by HPF is better than other fusion algorithm, which are superior to HPF in other application.

scholarly journals EEG-based classification of natural sounds reveals specialized responses to speech and music

2019 ◽  
Author(s):  
Nathaniel J Zuk ◽  
Emily S Teoh ◽  
Edmund C Lalor
Keyword(s):  
Classification Accuracy ◽  
Average Power ◽  
Cost Effective ◽  
List Type ◽  
Natural Sounds ◽  
Brain Responses ◽  
Species Specific ◽  
The Brain ◽  
Better Than

AbstractHumans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical “low-level” acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brain’s selectivity for speech and music in other populations.HighlightsEEG responses are stronger to speech and music than to other natural soundsThis selectivity was not replicated using stimuli with the same acoustic statisticsThese techniques can be a cost-effective way to study speech and music selectivity

Kepuasan Masyarakat Terhadap Pelayanan Kelurahan Setelah Pemekaran Di Tangerang Selatan

Liquidity ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 151-159
Author(s):  
Pitri Yandri
Keyword(s):  
Public Perception ◽  
Service Personnel ◽  
Urban Services ◽  
Hospitality Workers ◽  
Care Workers ◽  
Before And After ◽  
Cartesian Diagram ◽  
Better Than

The purpose of this study is (1) to analyze public perception on urban services before and after the expansion of the region, (2) analyze the level of people's satisfaction with urban services, and (3) analyze the determinants of the variables that determine what level of people's satisfaction urban services. This study concluded that first, after the expansion, the quality of urban services in South Tangerang City is better than before. Secondly, however, public satisfaction with the services only reached 48.53% (poor scale). Third, by using a Cartesian Diagram, the second priority that must be addressed are: (1) clarity of service personnel, (2) the discipline of service personnel, (3) responsibility for care workers; (4) the speed of service, (5) the ability of officers services, (6) obtain justice services, and (7) the courtesy and hospitality workers.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 233
Author(s):  
Dong-Woon Lee ◽  
Sung-Yong Kim ◽  
Seong-Nyum Jeong ◽  
Jae-Hong Lee
Keyword(s):  
Dental Implant ◽  
Classification Accuracy ◽  
Reliability And Validity ◽  
Mechanical Complication ◽  
Radiographic Images ◽  
Acceptable Accuracy ◽  
Reliable Detection ◽  
Classification Evaluation ◽  
Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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