RESEARCH OF THE PROCESS OF VISUAL ART TRANSMISSION IN MUSIC AND THE CREATION OF COLLECTIONS FOR PEOPLE WITH VISUAL IMPAIRMENTS
Bibliographic record
Abstract
This article explores the creation of music through the automated generation of sounds from images. The developed automatic image sound generation method is based on the joint use of neural networks and light-music theory. Translating visual art into music using machine learning models can be used to make extensive museum collections accessible to the visually impaired by translating artworks from an inaccessible sensory modality (sight) to an accessible one (hearing). Studies of other audio-visual models have shown that previous research has focused on improving model performance with multimodal information, as well as improving the accessibility of visual information through audio presentation, so the work process consists of two parts. The result of the work of the first part of the algorithm for determining the tonality of a piece is a graphic annotation of the transformation of the graphic image into a musical series using all colour characteristics, which is transmitted to the input of the neural network. While researching sound synthesis methods, we considered and analysed the most popular ones: additive synthesis, FM synthesis, phase modulation, sampling, table-wave synthesis, linear-arithmetic synthesis, subtractive synthesis, and vector synthesis. Sampling was chosen to implement the system. This method gives the most realistic sound of instruments, which is an important characteristic. The second task of generating music from an image is performed by a recurrent neural network with a two-layer batch LSTM network with 512 hidden units in each LSTM cell, which assembles spectrograms from the input line of the image and converts it into an audio clip. Twenty-nine compositions of modern music were used to train the network. To test the network, we compiled a set of ten test images of different types (abstract images, landscapes, cities, and people) on which the original musical compositions were obtained and stored. In conclusion, it should be noted that the composition generated from abstract images is more pleasant to the ear than the generation from landscapes. In general, the overall impression of the generated compositions is positive. Keywords: recurrent neural network, light music theory, spectrogram, generation of compositions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".