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Yıl 2022, Cilt: 5 Sayı: 2, 181 - 207, 31.08.2022

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

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

Öz

Kaynakça

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  • B. Karasulu, “Çoklu Ortam Sistemleri İçin Siber Güvenlik Kapsamında Derin Öğrenme Kullanarak Ses Sahne ve Olaylarının Tespiti,” Acta Infologica, vol. 3, no. 2, pp. 60-82, 2019.
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  • M.C. Yılmaz ve Z. Orman, "LSTM Derin Öğrenme Yaklaşımı ile Covid-19 Pandemi Sürecinde Twitter Verilerinden Duygu Analizi," Acta Infologica, vol. 5, no. 2, pp. 359-372. 2021.
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  • Y.-G. Jiang, Y. Wang, R. Feng, X. Xue, Y. Zheng, and H. Yang, “Understanding and Predicting Interestingness of Videos,” in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 1113–1119, 2013.
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An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology

Yıl 2022, Cilt: 5 Sayı: 2, 181 - 207, 31.08.2022

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

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

Öz

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.

Anahtar Kelimeler

Multimodal Deep Learning Association Rule Mining Named Entity Recognition Natural Language Processing

Kaynakça

  • B. Karakaya, E.B. Boztepe, and B. Karasulu, "Development of a Deep Learning Based Model for Recognizing the Environmental Sounds in Videos," in The SETSCI Conference Proceedings Book, vol. 5, no. 1, pp. 53-58, 2022.
  • B. Karasulu, “Çoklu Ortam Sistemleri İçin Siber Güvenlik Kapsamında Derin Öğrenme Kullanarak Ses Sahne ve Olaylarının Tespiti,” Acta Infologica, vol. 3, no. 2, pp. 60-82, 2019.
  • E. A. Kıvrak, B. Karasulu, C. Sözbir ve A. Türkay, “Ses Özniteliklerini Kullanan Ses Duygu Durum Sınıflandırma İçin Derin Öğrenme Tabanlı Bir Yazılımsal Araç,” Veri Bilim Dergisi, vol. 4, no. 3, pp.14-27, 2021.
  • S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a Convolutional Neural Network,” in Proceedings of the International Conference on Engineering and Technology (ICET), Antalya, Turkey, pp. 1-6, 2018.
  • Y. Zhao, X. Jin, and X. Hu, “Recurrent Convolutional Neural Network for Speech Processing,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5300-5304, 2017.
  • J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal Deep Learning,” in Proceedings of the 28th International Conference on Machine Learning (ICML11), Bellevue, Washington, USA, pp. 689–696, 2011.
  • S. Bird, E. Loper, and J. Baldridge, "Multidisciplinary Instruction with the Natural Language Toolkit," in Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics, Columbus, Ohio, pp. 62–70, 2008.
  • J. Joseph, and J. R. Jeba, "Information Extraction Using Tokenization And Clustering Methods," International Journal of Recent Technology and Engineering, vol. 8 no. 4, pp. 3680-3692, 2019.
  • H. van Halteren, J. Zavrel, and W. Daelemans, “Improving Accuracy in NLP Through Combination of Machine Learning Systems,” Computational Linguistics. vol. 27, no. 2, pp. 199–229, 2001.
  • A. Roy, “Recent Trends in Named Entity Recognition (NER),” arXiv preprint arXiv:2101.11420 [cs.CL], 2021.
  • K. Shaukat, S. Zaheer, and I. Nawaz, “Association Rule Mining: An Application Perspective,” International Journal of Computer Science and Innovation, vol. 2015, no. 1, pp.29-38, 2015.
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  • Ö. Şahinaslan, H. Dalyan ve E. Şahinaslan, "Naive Bayes Sınıflandırıcısı Kullanılarak YouTube Verileri Üzerinden Çok Dilli Duygu Analizi," Bilişim Teknolojileri Dergisi, vol. 15, no. 2, pp. 221-229, 2022.
  • M.C. Yılmaz ve Z. Orman, "LSTM Derin Öğrenme Yaklaşımı ile Covid-19 Pandemi Sürecinde Twitter Verilerinden Duygu Analizi," Acta Infologica, vol. 5, no. 2, pp. 359-372. 2021.
  • N. Buduma and N. Lacascio, Designing Next-Generation Machine Intelligence Algorithms Fundamentals of Deep Learning, O’Reilly Media UK Ltd., 2017.
  • F. Chollet, Deep Learning with Python, Manning Publications, 2017.
  • Y. Shen, C.-H. Demarty, and N.Q.K. Duong, “Deep Learning for Multimodal-Based Video Interestingness Prediction,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), pp. 1003-1008, 2017.
  • Y.-G. Jiang, Y. Wang, R. Feng, X. Xue, Y. Zheng, and H. Yang, “Understanding and Predicting Interestingness of Videos,” in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 1113–1119, 2013.
  • D. M. Agrawal, H. B. Sailor, M. H. Soni, and H. A. Patil, “Novel TEO-based Gammatone Features for Environmental Sound Classification,” in Proceedings of the 25th European Signal Processing Conference, pp.1859-1863, 2017.
  • Z. Mushtaq and S.-F. Su, “Efficient Classification of Environmental Sounds through Multiple Features Aggregation and Data Enhancement Techniques for Spectrogram Images,” Symmetry, vol. 12, no. 11:1822, pp. 1-34, 2020.
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  • A. Khamparia, D. Gupta, N.G. Nguyen, A. Khanna, B. Pandey, and P. Tiwari, “Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network,” IEEE Access, vol. 7, pp. 7717-7727, 2019.
  • K.J. Piczak, “Environmental sound classification with convolutional neural networks,” in Proceedings of the IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA pp. 1-6. 2015.
  • R. A. Khalil, E. Jones, M. I. Babar, T. Jan, M. Haseeb Z., and T. Alhussain, “Speech Emotion Recognition Using Deep Learning Techniques: A Review,” IEEE Access, vol. 7 pp. 117327-117345, 2019.
  • M. Gygli, H. Grabner, and L. V. Gool, “Video Summarization By Learning Submodular Mixtures Of Objectives,” in Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3090-3098, 2015.
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Toplam 94 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Emre Beray Boztepe 0000-0002-5925-5759

Bedirhan Karakaya 0000-0002-7255-9263

Bahadir Karasulu 0000-0001-8524-874X

İsmet Ünlü 0000-0002-6949-8666

Yayımlanma Tarihi 31 Ağustos 2022
Gönderilme Tarihi 2 Temmuz 2022
Kabul Tarihi 6 Temmuz 2022
Yayımlandığı Sayı Yıl 2022Cilt: 5 Sayı: 2

Kaynak Göster

IEEE E. B. Boztepe, B. Karakaya, B. Karasulu, ve İ. Ünlü, “An Approach for Audio-Visual Content Understanding of Video using Multimodal Deep Learning Methodology”, SAUCIS, c. 5, sy. 2, ss. 181–207, 2022, doi: 10.35377/saucis...1139765.
 Kapak Resmi İndir

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License