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Construction of an Art Education Teaching System Assisted by Artificial Intelligence

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 1010-1021页

作者： Wang, Xianyu Sun, Xiaoguang Henan Univ Engn Sch Fash Zhengzhou 451191 Peoples R China

With the continuous progress of art education and artificial intelligence technology, traditional music teaching models are facing transformation. This article aims to construct an art education and teaching system based on artificial intelligence, especially for teaching music sound recognition. Through in-depth research, we have designed a music sound recognition system that uses Mel frequency cepstral coefficient (mfcc) for feature parameter extraction, and combines BP neural network algorithm to construct a music sound learning model. The main purpose is to improve the efficiency and accuracy of music teaching through artificial intelligence technology. The main challenge we face in this process is how to effectively extract the features of music sounds and accurately identify different tones through algorithms. By using the mfcc algorithm, we have successfully solved this problem as it can effectively describe the time- frequency characteristics of music sound. Our proposed music sound learning model is based on a BP neural network, which trains the network to learn the mapping relationship between music sound and pitch. The experiment used piano sound as an example to verify the accuracy and reliability of the system. The simulation experiments conducted in MATLAB environment show that our system can accurately recognize and extract the main frequency of music, and has higher performance compared to traditional methods.

关键词： Feature extraction BP neural network Tone recognition smart art teaching MEL frequency cepstral coefficient mfcc algorithm time frequency characteristics

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Research on Health Monitoring based on Speech Signal Analysis Technology

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第2期8卷 345-355页

作者： Xingyu Chen Kexin Sun Hang Yuan

This paper aims to explore the technical application and implementation of speech recognition algorithm in health monitoring system. Speech recognition mainly uses DTW algorithm and mfcc algorithm, and cooperates with the collection and analysis of datasets to realize the extraction of user speech features and the recognition of health status. This paper will introduce the relevant background knowledge and concepts of speech recognition, analyse and explain the implementation of the algorithm, support the results and efficiency of the algorithm through MATLAB simulation, and finally discuss the advantages, disadvantages and application prospects of this research.

关键词： Speech Recognition Health Monitoring DTW algorithm mfcc algorithm MATLAB

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Systolic Murmurs Diagnosis Improvement by Feature Fusion and Decision Fusion

Systolic Murmurs Diagnosis Improvement by Feature Fusion and...

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IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA)

作者： Akbari, Somayeh Ghassemian, Hassan Akbari, Zahra Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781728133775

Acoustic sound generated by the heart mechanical activity, can provide useful information about the condition of heart valves. The heart sound auscultation is the fundamental tool in the evaluation of the cardiovascular system. The advantage of this method is fast, inexpensive and noninvasive. Due to human auscultatory limitation and non-stationary characteristics of phonocardiogram signals (PCG), diagnosis based on sounds that are heard via a stethoscope is difficult skill, therefor it requires a lot of practice. This study has proposed a biomedical automatic system for classification of PCG signals, which, recorded by a digital stethoscope. In order to extract various characteristics of PCG signals, the power spectrum estimation, wavelet transform (WT) and Mel frequency Cepstrum coefficients (mfcc) have been used in feature extraction step. Features are given to four classifiers: support vector machine (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP) and maximum likelihood (ML). The majority voting combination rule is utilized for fusion of different classifiers. The proposed method has been examined on dataset of 90 PCG records containing healthy and three types of cardiac valve diseases (pulmonary stenosis (PS), Atrial Septal Defect (ASD) and Ventricular Septal Defect (VSD)). The experimental results demonstrate that the classifier fusion rule significantly increases the diagnostic accuracy of abnormal PCG. Our proposed method can be used for online classification of PCG in intelligent diagnosis systems.

关键词： PCG signal mfcc algorithm power spectrum estimation wavelet transform feature fusion decision fusion

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Analysis of Asthma By Using Mel Frequency Cepstral Coefficient 1

Analysis of Asthma By Using Mel Frequency Cepstral Coefficie...

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IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Dighore, V. D. Thool, V. R. SGGS Inst Engn & Technol Dept Instrumentat Vishnupuri 431606 Nanded India

ISBN: (纸本)9781509007745

Asthma is a lung disease that affects airflow to and From the lungs. A whistling sound comes when a person suffering from asthma breathes in and out. Major symptoms of asthma are chest stiffness, breathe shortness and cough production during night and morning. In this paper, Asthma is analyze with the help of Mel frequency Cepstral Coefficient (mfcc). In this system, mfcc for Normal Voice and for Asthma patient voice is found out. The process of Feature extraction is that, in which the speaker is represented by the small amount of data from the voice signal. This system converts a speech waveform to type of parametric representation for further analysis and processing. There are two methods for mfcc feature extraction, either FFT based or LPC based. In this paper, mfcc extraction based on FFT is used.

关键词： Normal sound Asthma patient sound mfcc algorithm

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Analysis of Asthma By Using Mel Frequency Cepstral Coefficient

Analysis of Asthma By Using Mel Frequency Cepstral Coefficie...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： V. D. Dighore V. R. Thool Department of Instrumentation S.G.G.S Institute of Engineering & Technology Vishnupuri Nanded-431606

ISBN: (纸本)9781509007752

关键词： Normal sound Asthma patient sound mfcc algorithm

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Improved Window Function in the Application of mfcc Feature Parameter Extraction

Improved Window Function in the Application of MFCC Feature ...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Bai, Leqiang Zhang, Xuewei Shenyang Jianzhu Univ Informat & Control Engn Fac Shenyang Peoples R China

ISBN: (纸本)9783038351153

In view of spectrum leakage and the contradictory problem of spectrum accuracy of main lobe and reducing spectrum leakage, mfcc algorithm based on improved window function is proposed. Improved window function is based on the mathematical analysis of Kaiser window, and under the condition of finite sampling points minuses weighted impact function where is at the frequencies that side lobe peaks of correspond to. The amplitude of improved window compared with Kaiser window is smaller, and main lobe width is the same, solving the conflicting problem of main lobe width and side lobe amplitude and reducing spectrum leakage. The experimental results show that speech recognition rate of mfcc feature parameter extraction algorithm based on improved window function is better than Kaiser window and Hamming window.

关键词： mfcc algorithm spectrum leakage main lobe width side lobe amplitude speech recognition rate

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