Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

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A Study of Parameters Setting of the STADZT

Acta Polytechnica ◽

10.14311/1654 ◽

2012 ◽

Vol 52 (5) ◽

Author(s):

Václav Turoň

Keyword(s):

Spectral Analysis ◽

Fourier Transform ◽

Frequency Resolution ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Stationary Signals ◽

Zolotarev Polynomials ◽

Non Stationary Signal ◽

Short Time ◽

Main Influence

This paper deals with the new time-frequency Short-Time Approximated Discrete Zolotarev Transform (STADZT), which is based on symmetrical Zolotarev polynomials. Due to the special properties of these polynomials, STADZT can be used for spectral analysis of stationary and non-stationary signals with the better time and frequency resolution than the widely used Short-Time Fourier Transform (STFT). This paper describes the parameters of STADZT that have the main influence on its properties and behaviour. The selected parameters include the shape and length of the segmentation window, and the segmentation overlap. Because STADZT is very similar to STFT, the paper includes a comparison of the spectral analysis of a non-stationary signal created by STADZT and by STFT with various settings of the parameters.

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Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

Shock and Vibration ◽

10.1155/2016/6127479 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 48

Author(s):

Hongmei Liu ◽

Lianfeng Li ◽

Jian Ma

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Short Time Fourier Transform ◽

Bearing Fault Diagnosis ◽

Fault Features ◽

Sparse Autoencoder ◽

Stacked Sparse Autoencoder ◽

Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

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Deep Learning for Fault Diagnosis based on short-time Fourier transform

2018 International Conference on Smart Communications in Network Technologies (SaCoNeT) ◽

10.1109/saconet.2018.8585444 ◽

2018 ◽

Author(s):

Tarak BENKEDJOUH ◽

Noureddine ZERHOUNI ◽

Said RECHAK

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Fault Diagnosis ◽

Short Time Fourier Transform ◽

Short Time

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Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2016.04.009 ◽

2016 ◽

Vol 19 (3) ◽

pp. 1457-1464 ◽

Cited By ~ 16

Author(s):

Syed Khairul Bashar ◽

Mohammed Imamul Hassan Bhuiyan

Keyword(s):

Fourier Transform ◽

Empirical Mode Decomposition ◽

Motor Imagery ◽

Hybrid Method ◽

Short Time Fourier Transform ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition ◽

Short Time

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Automated Detection and Classification of Pavement Distresses using 3D Pavement Surface Images and Deep Learning

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211007481 ◽

2021 ◽

pp. 036119812110074

Author(s):

Rohit Ghosh ◽

Omar Smadi

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Three Dimensional ◽

Area Under The Curve ◽

Roc Curves ◽

Validation Dataset ◽

Accurate Identification ◽

Pavement Distresses ◽

High Level

Pavement distresses lead to pavement deterioration and failure. Accurate identification and classification of distresses helps agencies evaluate the condition of their pavement infrastructure and assists in decision-making processes on pavement maintenance and rehabilitation. The state of the art is automated pavement distress detection using vision-based methods. This study implements two deep learning techniques, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLO) v3, for automated distress detection and classification of high resolution (1,800 × 1,200) three-dimensional (3D) asphalt and concrete pavement images. The training and validation dataset contained 625 images that included distresses manually annotated with bounding boxes representing the location and types of distresses and 798 no-distress images. Data augmentation was performed to enable more balanced representation of class labels and prevent overfitting. YOLO and Faster R-CNN achieved 89.8% and 89.6% accuracy respectively. Precision-recall curves were used to determine the average precision (AP), which is the area under the precision-recall curve. The AP values for YOLO and Faster R-CNN were 90.2% and 89.2% respectively, indicating strong performance for both models. Receiver operating characteristic (ROC) curves were also developed to determine the area under the curve, and the resulting area under the curve values of 0.96 for YOLO and 0.95 for Faster R-CNN also indicate robust performance. Finally, the models were evaluated by developing confusion matrices comparing our proposed model with manual quality assurance and quality control (QA/QC) results performed on automated pavement data. A very high level of match to manual QA/QC, namely 97.6% for YOLO and 96.9% for Faster R-CNN, suggest the proposed methodology has potential as a replacement for manual QA/QC.

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