These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Arts and Technology (5 papers in press)
Multi-Note Intelligent Fusion Method Of Music Based On Artificial Neural Network by Ye Tian Abstract: In order to overcome the problems of low fusion efficiency and poor fusion quality of the traditional music multi-note fusion method, an intelligent fusion method of music multi-note based on artificial neural network is proposed. Establish a multi-note model, and analyse the basic signal structure of notes under this model. Collect the initial music audio file, and preprocess the audio file through the steps of parsing, noise reduction, filtering and calibration. Use artificial neural network to segment unit music, extract fundamental note, peak note and melody note, and output in the form of a single note. Extract the features of different notes, and realise the intelligent fusion of music multi-notes according to the feature extraction results. The experimental results show that, compared with the traditional fusion method, the fusion speed of the designed intelligent fusion method is increased by at least 12.6s, and the signal-to-noise ratio is increased by 1.23. Keywords: artificial neural network; music processing; multi-note fusion; contour curve.
Music Note Position Recognition in Optical Music Recognition using Convolutional Neural Network by Andrea Andrea, Paoline Paoline, Amalia Zahra Abstract: Technology improvement is rapidly changing. This impacts many fields, including music field. Technology has helped music field to be recognised in machine understanding. This field is called optical music recognition (OMR), a computer vision enabler in music. With OMR, we can define the position and music notation in music note. We propose a deep learning and convolutional neural network (CNN) approach to recognise a music position in music note. Music note position in staff is one of the keys to achieve pitch recognition. While we have music clef, key signature, and note position in staff, we can give machines the understanding of a note pitch. This experiment can bring and broaden the experiments in recognising music pitch, which take music note image as an input and position as the output. We use our own dataset and use CNN in experiments. This note position recognition experiment achieved 80% accuracy. Keywords: optical music recognition; OMR; music; pitch recognition; deep learning; convolutional neural network; CNN: music sheet. DOI: 10.1504/IJART.2021.10035633
Anita Bermeo by Galo Gallardo, Carmen Jijón, María Zarraga Abstract: For the creation of the play Keywords: La Torera; performance; biography; theatre; art; transmediality; transfictionality. DOI: 10.1504/IJART.2021.10035887
The method to capture the form of opera performance based on machine vision by Maoyuan Yin Abstract: In order to overcome the problems of traditional opera performance form capture methods, such as long denoising time and low capture accuracy, this paper proposes a new opera performance form capture method based on machine vision. The wavelet domain and gradient domain are obtained by multi-step local Wiener filtering in wavelet domain. The parameters of wavelet-based Wiener filter are modified to obtain a new wavelet threshold function. The grey images of different components of different opera performance form images are extracted for comparison, and the best component image is selected for threshold processing according to different opera performance forms. At the same time, image features are extracted in the form of binary image to realise the opera performance form capture based on machine vision. Experimental results show that the proposed method can quickly and accurately capture opera performance form, and the highest capture efficiency can reach 99.55%. Keywords: Machine vision; Opera performance; Form capture; Noise variance; Binary image. DOI: 10.1504/IJART.2021.10035891
Dynamic target image correction method of digital media based on virtual reality by TiAN Mi Abstract: In order to overcome the problems of low peak signal-to-noise ratio (PSNR), low structure similarity index and resolution coefficient not close to 1, a digital media dynamic target image correction method based on virtual reality is proposed. This method uses virtual reality, image neighbourhood information statistical information, fuzzy information and human visual characteristics to carry out grey value statistics, then combines with the statistical characteristics of grey value to realise image denoising processing; combined with rational function model, obtains the digital media dynamic target image pixel coordinates representing the ground point coordinates, obtains the target image correction model, completes the digital media dynamic target Image correction. The experimental results show that the peak signal-to-noise ratio (PSNR) is close to 400 dB, the maximum structure similarity index is 0.6025, and the decision coefficient is close to 1, which can quickly and accurately realise the dynamic target image correction of digital media. Keywords: virtual reality; digital media; dynamic target image; correction. DOI: 10.1504/IJART.2021.10038009