An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology

Emre Beray Boztepe; Bedirhan Karakaya; Bahadir Karasulu; İsmet Ünlü

doi:10.35377/saucis...1139765

EN

An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology

Abstract

This study contains an approach for recognizing the sound environment class from a video to understand the spoken content with its sentimental context via some sort of analysis that is achieved by the processing of audio-visual content using multimodal deep learning methodology. This approach begins with cutting the parts of a given video which the most action happened by using deep learning and this cutted parts get concanarated as a new video clip. With the help of a deep learning network model which was trained before for sound recognition, a sound prediction process takes place. The model was trained by using different sound clips of ten different categories to predict sound classes. These categories have been selected by where the action could have happened the most. Then, to strengthen the result of sound recognition if there is a speech in the new video, this speech has been taken. By using Natural Language Processing (NLP) and Named Entity Recognition (NER) this speech has been categorized according to if the word of a speech has connotation of any of the ten categories. Sentiment analysis and Apriori Algorithm from Association Rule Mining (ARM) processes are preceded by identifying the frequent categories in the concanarated video and helps us to define the relationship between the categories owned. According to the highest performance evaluation values from our experiments, the accuracy for sound environment recognition for a given video's processed scene is 70%, average Bilingual Evaluation Understudy (BLEU) score for speech to text with VOSK speech recognition toolkit's English language model is 90% on average and for Turkish language model is 81% on average. Discussion and conclusion based on scientific findings are included in our study.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Emre Beray Boztepe ^*
0000-0002-5925-5759
Türkiye

Bedirhan Karakaya
0000-0002-7255-9263
Türkiye

Bahadir Karasulu
0000-0001-8524-874X
Türkiye

İsmet Ünlü
0000-0002-6949-8666
Türkiye

Publication Date

August 31, 2022

Submission Date

July 2, 2022

Acceptance Date

July 6, 2022

Published in Issue

Year 1970 Volume: 5 Number: 2

DOI

https://doi.org/10.35377/saucis...1139765

IZ

https://izlik.org/JA34EU47DM

Cite

RIS / Bibtex

APA

Boztepe, E. B., Karakaya, B., Karasulu, B., & Ünlü, İ. (2022). An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology. Sakarya University Journal of Computer and Information Sciences, 5(2), 181-207. https://doi.org/10.35377/saucis...1139765

AMA

1.Boztepe EB, Karakaya B, Karasulu B, Ünlü İ. An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology. SAUCIS. 2022;5(2):181-207. doi:10.35377/saucis.1139765

Chicago

Boztepe, Emre Beray, Bedirhan Karakaya, Bahadir Karasulu, and İsmet Ünlü. 2022. “An Approach for Audio-Visual Content Understanding of Video Using Multimodal Deep Learning Methodology”. Sakarya University Journal of Computer and Information Sciences 5 (2): 181-207. https://doi.org/10.35377/saucis. 1139765.

EndNote

Boztepe EB, Karakaya B, Karasulu B, Ünlü İ (August 1, 2022) An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology. Sakarya University Journal of Computer and Information Sciences 5 2 181–207.

IEEE

[1]E. B. Boztepe, B. Karakaya, B. Karasulu, and İ. Ünlü, “An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology”, SAUCIS, vol. 5, no. 2, pp. 181–207, Aug. 2022, doi: 10.35377/saucis...1139765.

ISNAD

Boztepe, Emre Beray - Karakaya, Bedirhan - Karasulu, Bahadir - Ünlü, İsmet. “An Approach for Audio-Visual Content Understanding of Video Using Multimodal Deep Learning Methodology”. Sakarya University Journal of Computer and Information Sciences 5/2 (August 1, 2022): 181-207. https://doi.org/10.35377/saucis. 1139765.

JAMA

1.Boztepe EB, Karakaya B, Karasulu B, Ünlü İ. An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology. SAUCIS. 2022;5:181–207.

MLA

Boztepe, Emre Beray, et al. “An Approach for Audio-Visual Content Understanding of Video Using Multimodal Deep Learning Methodology”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 2, Aug. 2022, pp. 181-07, doi:10.35377/saucis. 1139765.

Vancouver

1.Emre Beray Boztepe, Bedirhan Karakaya, Bahadir Karasulu, İsmet Ünlü. An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology. SAUCIS. 2022 Aug. 1;5(2):181-207. doi:10.35377/saucis. 1139765