Recognition Of Music Notation Research Articles

Recognition of music symbol notation using convolutional neural network

Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried out by implementing vision transformer (ViT), CoAtNet-0, and ConvNeXt-Tiny architecture. The training process was also combined with data augmentation to provide more information for the model to learn. Then the accuracy results of each model were compared to find out the best model for the OMR solution in this research. This experiment uses the Andrea dataset and Attwenger dataset which both get the best result by using the augmentation method and ConvNeXt-Tiny as the model. The best accuracy for the Andrea dataset is 98.15% and for the Attwenger dataset is 98.43%.

Advancing Handwritten Musical Notation Recognition Using Deep Learning: A Convolutional Neural Network-Based Approach with Improved Accuracy

The use of computers to read musical scores is referred to as optical music recognition (OMR). The recent advancements in artificial intelligence and big data have led to the development of deep learning approaches for recognizing musical notes. Previous research has shown that there is a lot of room for improvement in handwritten musical notation recognition systems due to differences in writing styles and the complex structure of musical symbols. The research described here aims to develop a deep learning-based system for recognizing handwritten musical notation. The system uses a convolutional neural network (CNN) to extract and learn pixel features of musical symbols and achieve a recognition accuracy of over 90%. The CNN model was trained using image samples from the HOMUS dataset and fine-tuned to minimize the loss function and reduce classification errors. The CNN model achieved an accuracy of 96.95% on the test samples, which is a significant improvement over the 86.0% accuracy from previous studies. The performance of the CNN model was also compared to five state-of-the-art deep learning methods, namely, quantum gray Wolf optimization (QGWO) algorithm, nonfully connected network (NFC-Net) classifier, nearest neighbor classifier, data augmentation and ensemble learning, and the CNN model outperformed four of them. However, the CNN model occasionally misclassified musical symbols with similar shapes, indicating that there is still room for improvement in the system’s performance. Future research could focus on improving the model’s performance on similar-shaped symbols. Overall, the research demonstrates the effectiveness of using a CNN model for handwritten musical notation recognition and highlights the potential of deep learning approaches in this area.

Challenges of Teaching And Learning Visual Impairment Students In Music Notation Recognition Skills

Challenges of Teaching And Learning Visual Impairment Students In Music Notation Recognition Skills

Retracted: A Deep Learning-Based Piano Music Notation Recognition Method.

[This retracts the article DOI: 10.1155/2022/2278683.].

Generative Adversarial Network for Musical Notation Recognition during Music Teaching.

In order to improve the quality and efficiency of music teaching, we try to automate the teaching of music notation. With the addition of computer vision technology and note recognition algorithms, we improve the generative adversarial network to enhance the recognition accuracy and efficiency of music short scores. We adopt an embedded matching structure based on adversarial neural networks, starting from generators and discriminators, respectively, to unify generators and discriminators from the note input side. Each network layer is then laid out according to a cascade structure to preserve the different layers of note features in each convolutional layer. Residual blocks are then inserted in some network layers to break the symmetry of the network structure and enhance the ability of the adversarial network to acquire note features. To verify the efficiency of our method, we select monophonic spectrum, polyphonic spectrum, and miscellaneous spectrum datasets for validation. The experimental results demonstrate that our method has the best recognition accuracy in the monophonic spectrum and the miscellaneous spectrum, which is better than the machine learning method. In the recognition efficiency of note detail information, our method is more efficient in recognition and outperforms other deep learning methods.

Evrişimli Sinir Ağlarını Kullanarak Müzik Notasyonunu Tanıma

Musical scores are the essential of music theory and its development. Musical notation was developed by Greeks around 521 BCE, considering that music was developed a long time ago will will find a gap between new musical technology and old scrpits of music theory since they were written in. However, having music scores in written form has rised various kinds of problems for music information retrieval (MIR). Music notation recognition is a type of optical character recognition (OCR) applications, which allow us to recognize musical scores and convert it to a format that can be editied or played on computer such as musicXML (for page layout). In this paper, we introduce a Convolutional Neural Networks (CNN) based framework for musical notation recognition in images. We use a popular pre-trained CNN network, namely ResNet-101 to extract global features of notation and rest images. Then, a Support Vector Machine (SVM) is employed for training and classification purpose. ResNet-101 is one of the state-of-art pre-trained network for image recognition, ResNet-101 trained with more than a million images. Multiclass SVM classifiers using a fast-linear solver is also very powerful classifier. We also evaluated the proposed approach on a dataset that was derived from Attwenger, P RecordLabel and OMR-dataset, and then labeled manually by music theory. As a result, we can separate notes and rests from each other with an average accuracy of 99.02%. We can also classify five different note types. This is the first time that Resnet-101 and a SVM is combined together to perform musical notation recognition, and results are very promising.

A reliable and valid tool for measuring visual recognition ability with musical notation.

Recognizing musical notation is an important skill to a full participation of Western classical music, but remains a largely under-researched topic in the psychology of music. One plausible reason of such omission is that, in the past, the research field has heavily relied on self-report of music reading ability, which was subjective and highly variable. This paper presents a reliable and valid tool for objectively measuring individual abilities in visual recognition of musical notation. The visual fluency task measures how fast one can accurately recognize a sequence of musical notation at a desired accuracy level using the adaptive psychometric method QUEST. We checked the reliability of this task in over 200 participants in terms of Guttman's λ-2 and Cronbach's alpha. Also, we evaluated the construct validity of this task by considering the convergent validity of this task with multiple external real-world measures of one's musical training background, with numerous experimental measures of visual tendencies of musical notation recognition and with sight-reading performance. Overall, the visual fluency task achieved satisfactory reliability and validity for measuring abilities in recognizing musical notation. This opens the door for characterizing the cognitive mechanisms, development, and individual differences in musical notation recognition, for understanding music learning and music psychology and for understanding of visual perceptual expertise in general.

Recognition of pen-based music notation with finite-state machines

This work presents a statistical model to recognize pen-based music compositions using stroke recognition algorithms and finite-state machines. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives. Then, a Probabilistic Finite-State Automaton is obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that does not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis about the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest. Results found have shown the goodness of the proposed model, obtaining competitive performances in all metrics and scenarios considered.

The development of a music reading and singing two‐wheeled robot

The objective of this research was to construct a two‐wheeled robot that can autonomously read music and sing songs with a synthesized voice. The music for the robot was created by using a musical notation editor developed in this study, which could be read by the robot's vision system. A musical notation recognition program converted the image obtained from the vision system to a text file, and then a voice synthesis program processed the text file to generate the synthesized music autonomously. We tested four Mandarin songs and the accuracy of recognition was over 98%. It demonstrated that a robot with integrated functions should be promising in entertainment applications in the future.

Online Pen-Based Recognition of Music Notation with Artificial Neural Networks

Computer Music Journal, 27:2, pp. 70–79, Summer 2003 2003 Massachusetts Institute of Technology. There are many different reasons why we might want to enter music notation into a computer, including editing and composing tasks, educational music theory exercises, sophisticated searching of music archives based on melodic fragments supplied as search criteria, or simply producing transposed arrangements. Musicologists may be interested in analyzing the style of a collection, and copyright enforcers may be interested in detecting legal infringements. At present, musical information is most often entered into the computer using the computer keyboard, a mouse, a piano keyboard (or some other electronic instrument) attached to the computer, or a scanning device for a sheet of printed music with offline recognition of printed symbols. Various optical musical recognition (OMR) techniques have been developed to convert scanned pages of music into a machine-readable format. Blostein and Baird (1992) presented a critical survey of problems and approaches to music image analysis. Since then, work in the OMR field has continued with researchers such as Bainbridge and Carter (1997) and Bainbridge and Wijaya (1999), who wrote a system to convert optically scanned pages of music into a machine-readable format. More recently, Droettboom and Fujinaga (2001) created an adaptive music-recognition system and interpretation mechanism. Fujinaga and Riley (2002) concentrated upon recommendations and options of file formats in the context of creating an archival image containing all relevant data extracted from a printed score. This makes interpretation of the music for archival storing, web delivery, printing, and other applications possible. Other researchers have focused upon novel process techniques, such as Ng (2002), who recognized that a stroke-based segmentation approach using mathematical morphology is necessary in OMR, applied after the image pre-processing (i.e., thresholding, de-skewing, and basic layout analysis). Similar image-processing techniques have been explored for handwritten music (Roach and Tatem 1988; Ng 2001). In particular, the work conducted by Luth (2002) focused on the recognition of handwritten music manuscripts. It is based on imageprocessing algorithms like edge detection, skeletonization, and run-length. In addition, general image-processing methods have been explored that are applicable to both printed and handwritten music (George forthcoming). With online music input, the prevailing interface technology for music editing involves the conventional ‘‘point-and-click’’ mouse-based paradigm. The ‘‘point-and-click’’ method typically requires various musical symbols to be selected from a menu and meticulously placed on a staff, and a constant movement between the menu and the staff is necessary. A parallel in the context of word processing would be to individually select the alphabetical characters and place them on the page to compose a sentence. In other fields that require some written notation (whether signatures or postcodes, mathematical notations, or cursive writing), online input has moved away from ‘‘point-and-click’’ approaches to penbased recognition. However, with few exceptions, there is a noticeable absence of research into the pen-based recognition of music symbols. One common paradigm is simply using the pen as a stylus to select music symbols from a menu bar. In another paradigm, the user must learn a special sequence of movements to enter a given music symbol. The most desirable system, however, would allow the user to write conventional music symbols.

A Graph-Rewriting Paradigm for Discrete Relaxation

In image analysis, recognition of the primitives plays an important role. Subsequent analysis is used to interpret the arrangement of primitives. This subsequent analysis must make allowance for errors or ambiguities in the recognition of primitives. In this paper, we assume that the primitive recognizer produces a set of possible interpretations for each primitive. To reduce this primitive-recognition ambiguity, we use contextual information in the image, and apply constraints from the image domain. This process is variously termed constraint satisfaction, labeling or discrete relaxation. Existing methods for discrete relaxation are limited in that they assume a priori knowledge of the neighborhood model: before relaxation begins, the system is told (or can determine) which sets of primitives are related by constraints. These methods do not apply to image domains in which complex analysis is necessary to determine which primitives are related by constraints. For example, in music notation, we must recognize which notes belong to one measure, before it is possible to apply the constraint that the number of beats in the measure should match the time signature. Such constraints can be handled by our graph-rewriting paradigm for discrete relaxation: here neighborhood-construction is interleaved with constraint-application. In applying this approach to the recognition of simple music notation, we use approximately 180 graph-rewriting rules to express notational constraints and semantic-interpretation rules for music notation. The graph rewriting rules express both binary and higher-order notational constraints. As image-interpretation proceeds, increasingly abstract levels of interpretation are assigned to (groups of) primitives. This allows application of higher-level constraints, which can be formulated only after partial interpretation of the image.

A graph grammar programming style for recognition of music notation

Graph grammars are a promising tool for solving picture processing problems. However, the application of graph grammars to diagram recognition has been limited to rather simple analysis of local symbol configurations. This paper introduces the Build-Weed-Incorporate programming style for graph grammars and shows its application in determining the meaning of complex diagrams, where the interaction among physically distant symbols is semantically important. Diagram recognition can be divided into two stages: symbol recognition and high-level recognition. Symbol recognition has been studied extensively in the literature. In this work we assume the existence of a symbol recognizer and use a graph grammar to assemble the diagram's information content from the symbols and their spatial relationships. The Build-Weed-Incorporate approach is demonstrated by a detailed discussion of a graph grammar for high-level recognition of music notation.

Laterality effects with visual perception of musical chords and dot patterns.

Using the paradigm denoting a contralateral relationship between visual field and brain hemisphere, laterality effects with music notation and dots were investigated. Two groups of readers of advanced piano music were selected, one good, one poor. Each group participated in two visual half-field experiments, one using random dot patterns and the other using musical chords. Significant opposite field effects were found for the two kinds of stimuli across reading groups, the left field for dots and the right field for chords. With dots, a significant sex effect emerged, with males superior to females across groups. The only significant group difference appeared in the chord experiment, in which the good group was superior. A practice effect also was found only with chords. Results suggest that the left hemisphere is more important for recognition of music notation than the right.