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

Calvo-Zaragoza, Jorge; Oncina, José

doi:10.1016/j.eswa.2016.10.041

Cited by 11 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of main melody track extraction, Fukayama and Goto focused on the MIDI music feature extraction before classification, the unbalanced situation of classification samples, and the reliability of classification results after two classifications so as to ensure the main melody track of MIDI music [ 9 ]. Calvo-Zaragoza and Oncina used SVM as a classifier to classify six different styles of music, namely pop music, classical instruments, piano music, folk songs, bel canto, and opera [ 10 ]. Chen et al studied the theme judgment algorithm and proposed a theme judgment algorithm combining rules and statistical methods [ 1 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Extraction of Music Main Melody and Multi‐Pitch Estimation Method Based on Support Vector Machine in Big Data Environment

Liang

Shu

2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

Main melody extraction and multi-pitch estimation are two important research topics in the MIR field. In this article, the SVM algorithm is used to analyze and discuss music melody extraction and multi-pitch estimation. In the part of multi-fundamental frequency extraction, this article first filters the song signal with equal loudness and weakens the energy of the high-frequency and low-frequency parts of the song signal. Thereafter, the multi-resolution short-time Fourier transform suitable for processing song signals is introduced. In addition, in order to avoid the sharp jump of the estimated melody pitch in the same note duration range, this article proposes a main melody extraction method combining the SVM algorithm with dynamic programming. In this article, more features are used to distinguish the pitch contour of vocal fundamental frequency from that of the nonvocal fundamental frequency, which does not only depend on energy or a certain feature. The experimental results show that the lowest octave error of this method is 1.46. Meanwhile, the recall rate of the algorithm can reach about 95%. This method not only improves the recall rate of the fundamental frequency of the human voice but also improves the recall rate and pitch accuracy rate of the whole main melody extraction system.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Extraction of Music Main Melody and Multi‐Pitch Estimation Method Based on Support Vector Machine in Big Data Environment

Liang

Shu

2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…For this problem, the researchers used computer vision techniques for the same speciﬁcation. Each handwritten music notation has a different representation, and different types of music notation cannot be processed with the same heuristic [ 7 ]. According to the frequency of music notation usage, the scanned music notation needs to be reconstructed phonetically, and the music notation is assigned according to different weights as a way to cater to the fluency of the music notation.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

“…We make use of previous works already done on this task to learn the set of 495 stroke primitives and the sequence of stroke primitives that define each musical symbol [31,32]. In that case, the probabilities of the edges are computed from the probability of each input stroke to be each of the considered stroke primitives.…”

Section: Segmentation-free Scenariomentioning

confidence: 99%

An efficient approach for Interactive Sequential Pattern Recognition

Calvo-Zaragoza

Oncina

2017

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

Interactive Pattern Recognition (IPR) is an emergent framework in which the user is involved actively in the recognition process by giving feedback to the system when an error is detected. Although this framework is expected to reduce the number of errors to correct, it may increase the time required to complete the task since the machine needs to recompute its proposal after each interaction. Therefore, a fast computation is required to make the interactive system profitable and user-friendly. This work presents an efficient approach to deal with IPR tasks when data has a sequential nature. Our approach includes some computation at the very beginning of the task but it then achieves a linear complexity after user corrections. We also show how these tasks can be effectively carried out if the solution space is defined with a Regular Language. This fact has indeed proven to be the most relevant factor to improve the efficiency of the approach. Several experiments are carried out in which our proposal is faced against a classical search. Results show a reduction in time in all experiments considered, solving efficiently some complex IPR tasks thanks to our proposals.

show abstract

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

Cited by 11 publications

References 31 publications

[Retracted] Extraction of Music Main Melody and Multi‐Pitch Estimation Method Based on Support Vector Machine in Big Data Environment

[Retracted] Extraction of Music Main Melody and Multi‐Pitch Estimation Method Based on Support Vector Machine in Big Data Environment

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

An efficient approach for Interactive Sequential Pattern Recognition

Contact Info

Product

Resources

About