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

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...

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Main Authors: Wei Jiang, Dong Sun
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Advances in Mathematical Physics
Online Access:http://dx.doi.org/10.1155/2021/3148747
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spelling doaj-6a716095a5eb42998bd61f64725ed16a2021-10-11T00:39:57ZengHindawi LimitedAdvances in Mathematical Physics1687-91392021-01-01202110.1155/2021/3148747Music Signal Recognition Based on the Mathematical and Physical Equation Inversion MethodWei Jiang0Dong Sun1Department of MusicSchool of Electronic and Information EngineeringDigitization 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.http://dx.doi.org/10.1155/2021/3148747
collection DOAJ
language English
format Article
sources DOAJ
author Wei Jiang
Dong Sun
spellingShingle Wei Jiang
Dong Sun
Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
Advances in Mathematical Physics
author_facet Wei Jiang
Dong Sun
author_sort Wei Jiang
title Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
title_short Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
title_full Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
title_fullStr Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
title_full_unstemmed Music Signal Recognition Based on the Mathematical and Physical Equation Inversion Method
title_sort music signal recognition based on the mathematical and physical equation inversion method
publisher Hindawi Limited
series Advances in Mathematical Physics
issn 1687-9139
publishDate 2021-01-01
description 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.
url http://dx.doi.org/10.1155/2021/3148747
work_keys_str_mv AT weijiang musicsignalrecognitionbasedonthemathematicalandphysicalequationinversionmethod
AT dongsun musicsignalrecognitionbasedonthemathematicalandphysicalequationinversionmethod
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