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.

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.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Energy-Related Behaviour of Consumers from the Silesia Province (Poland)—Towards a Low-Carbon Economy

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2218
Author(s):  
Sylwia Słupik ◽  
Joanna Kos-Łabędowicz ◽  
Joanna Trzęsiok
Keyword(s):  
Classification Model ◽  
Attitudes And Beliefs ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
The Public ◽  
Second Stage ◽  
Initial Stage ◽  
Low Emission ◽  
Energy Behaviour ◽  
Carbon Economy

The issue of energy behaviour among Polish consumers, and especially the motives and attitudes they manifest, is relatively under-researched. This article attempts to identify individual attitudes and beliefs of energy consumers using the example of the residents of the province of Silesia (Poland). The authors conducted the expert segmentation of respondents in terms of their motivation for saving energy, based on the results of their proprietary survey. The second stage of the study involved using a classification model that allowed for the characterisation of the obtained groups. Psychological and financial factors were of greatest significance, which is confirmed by the results of other studies. Nonetheless, the obtained results explicitly indicate the specificity of the region, which requires transformation towards a low-emission economy. Despite the initial stage of changes both in the awareness of the consumers and the public interventions of the authorities, it should be emphasized that a majority of the respondents—at least to a basic extent—declared taking energy-saving measures. Financial motives are predominant among the respondents, although pro-environmental motives can also be noticed, which might translate into increased involvement and concern for the environment and climate.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

Critical Analysis of Methods for Assessing the Innovation Potential of an Enterprise

2021 ◽  
Vol 9 (1) ◽  
pp. 28-35
Author(s):  
Mariya Podshivalova ◽  
S. Almrshed
Keyword(s):  
Critical Analysis ◽  
Graphical Representation ◽  
Assessment Tools ◽  
Classification Model ◽  
Second Stage ◽  
Innovation Potential ◽  
Economic Statistics ◽  
Starting Point ◽  
The Subject ◽  
Innovative Capacity

The starting point of research on assessing the innovative capacity of an enterprise is the question of definitions. In this regard, authors initially turned to review of scientific literature on the subject of definitions variety for the term "enterprise innovative capacity". These data show that the wording of this term by both foreign and Russian researchers differs significantly. Authors propose a systematization of approaches to the definition and a corresponding graphical classification model, which highlights the evolutionary, resource, functional and process approaches. Further, a critical analysis of approaches to assessing enterprise innovative capacity is carried out. At the first stage, the content of modern assessment methods was studied, and at the second stage, the mathematical tools used were studied. Authors have formed a graphical representation of critical analysis results and based on it, they have concluded that among the approaches to assessing enterprise innovative capacity, the evolutionary approach should be recognized as promising, and among the methods of quantitative assessment – tools of economic statistics.

A Supervised Learning Model for AGV Perception in Unstructured Environment

2015 ◽  
pp. 334-353
Author(s):  
Rizwan Aqeel ◽  
Saif Ur Rehman ◽  
Saira Gillani ◽  
Sohail Asghar
Keyword(s):  
Clustering Algorithm ◽  
Research Problem ◽  
Classification Model ◽  
Support Vector ◽  
Ground Vehicle ◽  
Second Stage ◽  
Initial Stage ◽  
Cluster Area ◽  
Proposed Model ◽  
Unstructured Environment

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) - preprocessing has been performed in the initial stage; (2) - road area clustering has been done in the second stage; (3) - Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

A feature complexity index

1998 ◽  
Vol 212 (5) ◽  
pp. 405-412 ◽  
Author(s):  
G Little ◽  
R Tuttle ◽  
D E R Clark ◽  
J Corney
Keyword(s):  
Feature Recognition ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Solid Model ◽  
Geometric Complexity ◽  
Relative Complexity ◽  
Complexity Index

An index is presented for quantifying the geometric complexity of a three-dimensional solid model. This provides a measure by which components may be compared one with another in relation to their relative complexity. The index is alphanumeric and readily computable. Such an index can be of use in the field of feature recognition as a means to determine how efficiently one algorithm handles components of varying complexity compared to another such algorithm. The performance of the authors’ own feature recognition algorithm is tested against components of differing complexity as determined by the index.

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