Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Автор: Arshia Foroozan Yazdani, Ali Bozorgi Mehr, Iman Showkatyan, Amin Hashemi, Mohsen Kakavand

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 1 vol.13, 2021 года.

Бесплатный доступ

The present study, the main idea of which was based on one of the questions of I.P.T.2018 competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection tool. In fact, this paper was provided based on a traditional phenomenological project using the well-known deep neural networks, in order to achieve an acceptable accuracy in this project. In order to improve the quality of the paper, the data were analyzed in two ways: I. Using the images of data spectrogram and the known V.G.G.16 network. II. Applying the data audio signal and a convolutional neural network (C.N.N.). Finally, both methods have obtained an acceptable precision above 85%.

Еще

Fluid temperature, data spectrogram, V.G.G.16 (Visual Geometry Group), C.N.N (Convolutional Neural Network)., the environmental sounds, sound classification, physical-computational research, Deep learning

Короткий адрес: https://sciup.org/15017382

IDR: 15017382   |   DOI: 10.5815/ijigsp.2021.01.03

Список литературы Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

  • Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics. 1980;36(4):193-202.
  • LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation applied to handwritten zip code recognition. Neural computation. 1989;1(4):541-51.
  • Wang W, Lo H-K. Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution. arXiv preprint arXiv:181207724. 2018.
  • Lu F-Y, Yin Z-Q, Wang C, Cui C-H, Teng J, Wang S, et al. Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network. JOSA B. 2019;36(3):B92-B8.
  • Nautrup HP, Delfosse N, Dunjko V, Briegel HJ, Friis N. Optimizing quantum error correction codes with reinforcement learning. arXiv preprint arXiv:181208451. 2018.
  • Beach MJ, De Vlugt I, Golubeva A, Huembeli P, Kulchytskyy B, Luo X, et al. QuCumber: wavefunction reconstruction with neural networks. arXiv preprint arXiv:181209329. 2018.
  • Ramezanpour A. Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm. Physical Review A. 2018;98(6):062309.
  • Noé F, Wu H. Boltzmann generators-sampling equilibrium states of many-body systems with deep learning. arXiv preprint arXiv:181201729. 2018.
  • Giannetti C, Lucini B, Vadacchino D. Machine Learning as a universal tool for quantitative investigations of phase transitions. Nuclear Physics B. 2019;944:114639.
  • Schäfer F, Lörch N. Divergence of predictive model output as indication of phase transitions. arXiv preprint arXiv:181200895. 2018.
  • Kashiwa K, Kikuchi Y, Tomiya A. Phase transition encoded in neural network. Progress of Theoretical and Experimental Physics. 2019;2019(8).
  • Quek Y, Fort S, Ng HK. Adaptive Quantum State Tomography with Neural Networks. arXiv preprint arXiv:181206693. 2018.
  • Nichols R, Mineh L, Rubio J, Matthews JC, Knott PA. Designing quantum experiments with a genetic algorithm. arXiv preprint arXiv:181201032. 2018.
  • O’Driscoll L, Nichols R, Knott P. A hybrid machine learning algorithm for designing quantum experiments. Quantum Machine Intelligence. 2019;1(1-2):5-15.
  • Sorteberg WE, Garasto S, Pouplin AS, Cantwell CD, Bharath AA. Approximating the solution to wave propagation using deep neural networks. arXiv preprint arXiv:181201609. 2018.
  • Ming Y, Lin C-T, Bartlett SD, Zhang W-W. Quantum topology identification with deep neural networks and quantum walks. arXiv preprint arXiv:181112630. 2018.
  • Varsamopoulos S, Bertels K, Almudever CG. Designing neural network-based decoders for surface codes. arXiv preprint arXiv:181112456. 2018.
  • Bohrdt A, Chiu CS, Ji G, Xu M, Greif D, Greiner M, et al. Classifying snapshots of the doped hubbard model with machine learning. Nature Physics. 2019:1-4.
  • Flurin E, Martin LS, Hacohen-Gourgy S, Siddiqi I. Using a recurrent neural network to reconstruct quantum dynamics of a superconducting qubit from physical observations. arXiv preprint arXiv:181112420. 2018.
  • Goodfellow I, Bengio Y, Courville A. Deep learning: MIT press; 2016.
  • Wang J, Ma Y, Zhang L, Gao RX, Wu D. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems. 2018;48:144-56.
  • Ng A, editor what data scientists should know about deep learning. Extract data conference; 2017 march: Baidu Research.
  • Ciaburro G, Venkateswaran B. Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles: Packt Publishing Ltd; 2017.
  • Gollapudi S. Deep Learning for Computer Vision. Learn Computer Vision Using OpenCV: Springer; 2019. p. 51-69.
  • Gulli A, Pal S. Deep Learning with Keras: Packt Publishing Ltd; 2017.
  • Thampi SM, Mitra S, Mukhopadhyay J, Li K-C, James AP, Berretti S. Intelligent Systems Technologies and Applications: Springer; 2018.
  • Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. arXiv preprint arXiv:14042188. 2014.
  • Howard AG. Some improvements on deep convolutional neural network-based image classification. arXiv preprint arXiv:13125402. 2013.
  • CireşAn D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural networks. 2012;32:333-8.
  • Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet V. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:13126082. 2013.
  • Sermanet P, Chintala S, LeCun Y. Convolutional neural networks applied to house numbers digit classification. arXiv preprint arXiv:12043968. 2012.
  • Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Medical physics. 2017;44(12):6377-89.
  • Ciresan D, Giusti A, Gambardella LM, Schmidhuber J, editors. Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systems; 2012.
  • Lee H, Pham P, Largman Y, Ng AY, editors. Unsupervised feature learning for audio classification using convolutional deep belief networks. Advances in neural information processing systems; 2009.
  • Deng L, Abdel-Hamid O, Yu D, editors. A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. ICASSP; 2013.
  • Abdel-Hamid O, Mohamed A-r, Jiang H, Deng L, Penn G, Yu D. Convolutional neural networks for speech recognition. IEEE/ACM Transactions on audio, speech, and language processing. 2014;22(10):1533-45.
  • Sainath TN, Mohamed A-r, Kingsbury B, Ramabhadran B, editors. Deep convolutional neural networks for LVCSR. 2013 IEEE international conference on acoustics, speech and signal processing; 2013: IEEE.
  • Abdel-Hamid O, Mohamed A-r, Jiang H, Penn G, editors. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. 2012 IEEE international conference on Acoustics, speech and signal processing (ICASSP); 2012: IEEE.
  • Abdel-Hamid O, Deng L, Yu D, editors. Exploring convolutional neural network structures and optimization techniques for speech recognition. Interspeech; 2013.
  • Deng L, Li J, Huang J-T, Yao K, Yu D, Seide F, et al., editors. Recent advances in deep learning for speech research at Microsoft. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing; 2013: IEEE.
  • Noman F, Ting C-M, Salleh S-H, Ombao H, editors. Short-segment heart sound classification using an ensemble of deep convolutional neural networks. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2019: IEEE.
  • Barchiesi D, Giannoulis D, Stowell D, Plumbley MD. Acoustic scene classification: Classifying environments from the sounds they produce. IEEE Signal Processing Magazine. 2015;32(3):16-34.
  • Chachada S, Kuo C-CJ. Environmental sound recognition: A survey. APSIPA Transactions on Signal and Information Processing. 2014;3.
  • Piczak KJ, editor Environmental sound classification with convolutional neural networks. 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP); 2015: IEEE.
  • Zhu B, Xu K, Wang D, Zhang L, Li B, Peng Y, editors. Environmental sound classification based on multi-temporal resolution convolutional neural network combining with multi-level features. Pacific Rim Conference on Multimedia; 2018: Springer.
  • Piczak KJ, editor ESC: Dataset for environmental sound classification. Proceedings of the 23rd ACM international conference on Multimedia; 2015: ACM.
  • Zhu B, Wang C, Liu F, Lei J, Huang Z, Peng Y, et al., editors. Learning environmental sounds with multi-scale convolutional neural network. 2018 International Joint Conference on Neural Networks (IJCNN); 2018: IEEE.
  • Medhat F, Chesmore D, Robinson J, editors. Masked conditional neural networks for environmental sound classification. International Conference on Innovative Techniques and Applications of Artificial Intelligence; 2017: Springer.
  • Dieleman S, Brakel P, Schrauwen B, editors. Audio-based music classification with a pretrained convolutional network. 12th International Society for Music Information Retrieval Conference (ISMIR-2011); 2011: University of Miami.
  • Van den Oord A, Dieleman S, Schrauwen B, editors. Deep content-based music recommendation. Advances in neural information processing systems; 2013.
  • Medhat F, Chesmore D, Robinson J, editors. Automatic classification of music genre using masked conditional neural networks. 2017 IEEE International Conference on Data Mining (ICDM); 2017: IEEE.
  • Nasrullah Z, Zhao Y. Music Artist Classification with Convolutional Recurrent Neural Networks. arXiv preprint arXiv:190104555. 2019.
  • Feng L, Liu S, Yao J. Music genre classification with paralleling recurrent convolutional neural network. arXiv preprint arXiv:171208370. 2017.
  • Clark S, Park D, Guerard A. Music genre classification using machine learning techniques. Citeseer; 2012.
  • Shuvaev S, Giaffar H, Koulakov AA. Representations of sound in deep learning of audio features from music. arXiv preprint arXiv:171202898. 2017.
  • Hussain M, Haque MA. Swishnet: a fast convolutional neural network for speech, music and noise classification and segmentation. arXiv preprint arXiv:181200149. 2018.
  • Al-Shemmeri T. Engineering fluid mechanics: Bookboon; 2012.
  • Becker S, Ackermann M, Lapuschkin S, Müller K-R, Samek W. Interpreting and explaining deep neural networks for classification of audio signals. arXiv preprint arXiv:180703418. 2018.
  • Dahan E, Keller Y. SelfKin: Self Adjusted Deep Model For Kinship Verification. arXiv preprint arXiv:180908493. 2018.
  • Abadi M, editor TensorFlow: learning functions at scale. Acm Sigplan Notices; 2016: ACM.
  • Nagi J, Ducatelle F, Di Caro GA, Cireşan D, Meier U, Giusti A, et al., editors. Max-pooling convolutional neural networks for vision-based hand gesture recognition. 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA); 2011: IEEE.
  • Lin M, Chen Q, Yan S, editors. Network in network. arXiv preprint arXiv: 13124400 2013. Workshop on Statistical Atlases and Computational Models of the Heart Springer; 2011.
Еще
Статья научная