Optical Music Recognition using Image Processing and Machine Learning

Mathew, Prince; Vijayakumar, R.; Kuriakose, Aju Tom; Sunny, Jesmy; Bai, Ramani

doi:10.26438/ijcse/v6si10.1823

Cited by 2 publications

(2 citation statements)

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“…The data sharing module is the first and most important part of designing an event recognition system [ 24 , 25 ]. To measure effectiveness and create a class, the initial database is usually divided into a training program, a verification program, and a set of tests.…”

Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

confidence: 99%

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.

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Section: Research Methods Of Speech Recognition Technology In Music S...mentioning

confidence: 99%

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Bai

2022

Computational Intelligence and Neuroscience

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“…How to organically combine the art of music with computer science, that is, to automatically convert the images of paper scores to facilitate computer recognition and performance, has become an urgent problem to be solved 5 . Meng, F introduced a new notation called bootleg notation, which included the position of the points of the notes relative to the staff.…”

Section: Introductionmentioning

confidence: 99%

Automatic conversion system of digital music score image based on image recognition algorithm

Yao

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method.

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Optical Music Recognition using Image Processing and Machine Learning

Cited by 2 publications

References 1 publication

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

Automatic conversion system of digital music score image based on image recognition algorithm

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