Research on Crane Sound Clustering of MFCC Based on HHT

Fu, Qinjian; Lv, Danjue; Zhang, Yan; Xu, Haifeng; Wang, Yao; Zi, Jiali; Liu, Jiang

doi:10.1088/1742-6596/1693/1/012134

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the two songs that need to be matched, feature extraction is to extract some salient features from them. The song melody features mainly include four feature vectors of pitch, length, speed, and dynamics [13,14].…”

Section: Intelligent Scoring Of the Melody Model Singing Training Bas...mentioning

confidence: 99%

Modeling Method of Intelligent Scoring for Solfeggio Training Based on Deep Learning

2022

Mobile Information Systems

View full text Add to dashboard Cite

In the solfeggio training, melody model singing is an important part of auditory training. Reasonable and effective model singing training is of great significance to train the singer’s hearing. So, the level of model singing also reflects the level of the singer’s solfeggio to a certain extent. The correct model singing score will help students to master their own real melody model singing level and help students to better improve their solfeggio skills. However, the melody model singing training scoring system that has been developed has low accuracy and needs to be improved. In order to improve the accuracy of the intelligent scoring results of melody model singing training, this paper used deep learning technology to study the intelligent scoring method of melody model singing training. This paper first extracted the features of songs, then collected a large number of song features, trained the existing feature data, and established an intelligent scoring model for melody model singing training. Then, the students’ model songs and standard songs were used as two inputs, and the similarity of the characteristics of the two songs was analyzed; then, the score results of the model songs were obtained. The validity of this research should be verified by a comparative experiment. The research results showed that the melody model singing training intelligent scoring model, established by deep learning technology, has a higher accuracy rate, and the accuracy rate was increased by 6.82% compared with the original scoring model, indicating the research has practical significance.

show abstract

Section: Intelligent Scoring Of the Melody Model Singing Training Bas...mentioning

confidence: 99%

Modeling Method of Intelligent Scoring for Solfeggio Training Based on Deep Learning

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

Mel Frequency Cepstral Coefficient and its Applications: A Review

Abdul

Al-Talabani

2022

IEEE Access

View full text Add to dashboard Cite

Feature extraction and representation has significant impact on the performance of any machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to model features of audio signal and is widely used in various fields. This paper aims to review the applications that the MFCC is used for in addition to some issues that facing the MFCC computation and its impact on the model performance. These issues include the use of MFCC for non-acoustic signals, adopting the MFCC alone or combining it with other features, the use of time series versus global representation of the MFCC, following the standard form of the MFCC computation versus modifying its parameters, and supplying the traditional machine learning methods versus the deep learning methods..

show abstract

Research on Crane Sound Clustering of MFCC Based on HHT

Cited by 2 publications

References 0 publications

Modeling Method of Intelligent Scoring for Solfeggio Training Based on Deep Learning

Modeling Method of Intelligent Scoring for Solfeggio Training Based on Deep Learning

Mel Frequency Cepstral Coefficient and its Applications: A Review

Contact Info

Product

Resources

About