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Journal articles on the topic 'Fourier spectrogram'
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Pethiyagoda, Ravindra, Scott W. McCue, and Timothy J. Moroney. "Spectrograms of ship wakes: identifying linear and nonlinear wave signals." Journal of Fluid Mechanics 811 (December 6, 2016): 189–209. http://dx.doi.org/10.1017/jfm.2016.753.
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A spectrogram is a useful way of using short-time discrete Fourier transforms to visualise surface height measurements taken of ship wakes in real-world conditions. For a steadily moving ship that leaves behind small-amplitude waves, the spectrogram is known to have two clear linear components, a sliding-frequency mode caused by the divergent waves and a constant-frequency mode for the transverse waves. However, recent observations of high-speed ferry data have identified additional components of the spectrograms that are not yet explained. We use computer simulations of linear and nonlinear ship wave patterns and apply time–frequency analysis to generate spectrograms for an idealised ship. We clarify the role of the linear dispersion relation and ship speed on the two linear components. We use a simple weakly nonlinear theory to identify higher-order effects in a spectrogram and, while the high-speed ferry data are very noisy, we propose that certain additional features in the experimental data are caused by nonlinearity. Finally, we provide a possible explanation for a further discrepancy between the high-speed ferry spectrograms and linear theory by accounting for ship acceleration.
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Wen, X., and M. Sandler. "Composite spectrogram using multiple Fourier transforms." IET Signal Processing 3, no. 1 (2009): 51. http://dx.doi.org/10.1049/iet-spr:20070015.
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Wen-kai Lu and Qiang Zhang. "Deconvolutive Short-Time Fourier Transform Spectrogram." IEEE Signal Processing Letters 16, no. 7 (July 2009): 576–79. http://dx.doi.org/10.1109/lsp.2009.2020887.
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Trufanov, N. N., D. V. Churikov, and O. V. Kravchenko. "Selection of window functions for predicting the frequency pattern of vibrations of the technological process using an artificial neural network." Journal of Physics: Conference Series 2091, no. 1 (November 1, 2021): 012074. http://dx.doi.org/10.1088/1742-6596/2091/1/012074.
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Abstract The frequency pattern of the process is investigated by analyzing spectrograms constructed using the window Fourier transform. A set of window functions consists of a rectangular, membership, and windows based on atomic functions. The fulfillment of the condition for improving the time localization and energy concentration in the central part of the window allows one to select a window function. The resulting spectrograms are fed to the input of an artificial neural network to obtain a forecast. Varying the shape of the window functions allows us to analyze the proposed spectrogram prediction model.
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Lyon, Douglas. "The Discrete Fourier Transform, Part 5: Spectrogram." Journal of Object Technology 9, no. 1 (2010): 15. http://dx.doi.org/10.5381/jot.2010.9.1.c2.
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Dusek, Daniel. "Decomposition of Non-Stationary Signals Based on the Cochlea Function Principle." Solid State Phenomena 147-149 (January 2009): 594–99. http://dx.doi.org/10.4028/www.scientific.net/ssp.147-149.594.
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This paper deal with possibility of cochlea function principle utilization for decomposition any non-stationary signals. The mathematical model based on array of resonators is described in this paper. This array of resonators is actuated by non-stationary signal, which is compound from different frequency components. Spectrograms calculated for different values of resonators viscous damping are results of this work and this results are also compared with spectrogram obtained from Short Time Fourier Transformation (STFT).
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Lu, Wenkai, and Fangyu Li. "Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram." GEOPHYSICS 78, no. 2 (March 1, 2013): V43—V51. http://dx.doi.org/10.1190/geo2012-0125.1.
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The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.
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Palupi, Indiati Retno, and Wiji Raharjo. "The Utilization of Signal Analysis by Using Short Time Fourier Transform." RSF Conference Series: Engineering and Technology 1, no. 1 (December 23, 2021): 30–36. http://dx.doi.org/10.31098/cset.v1i1.445.
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Signal Analysis is a part of geophysics work. It is important in analyse the character of signal or waveform in geophysics. In this paper the earthquake waveform is used as the example. One method to do this is used Short Time Fourier Transform. It adopts the basic concept of Fast Fourier Transform in the short period of time in waveform and at the same moment there is a convolutional process between the waveform and the mother wavelet and then resulting the spectrogram. Finally, the spectrogram will show the power spectrum or the magnitude of the amplitude in each time in the waveform. It relates with the energy of the earthquake. The result including three parameters, they are time, frequency and the spectrogram. It makes easier for the geophysicist to analyse the frequency changing in each time based on the spectrogram colour. Besides that, it can be used to identify the arrival time of P and S wave as the important information in calculate the hypocentre location of the earthquake.
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Neralla, Manikanta. "Design and Performance Analysis of Short Time Fourier Transform Processor." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3205–15. http://dx.doi.org/10.22214/ijraset.2022.41917.
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Abstract: Time-frequency domain characterization of signals have always been focused on variants of Short time Fourier transform (STFT). The selection of transform kernel plays an important role in preserving the signal support which provides a cross-term free time-frequency distribution. Time-Bandwidth product has been taken as a measure of signal support preservation criteria thereby developing an optimal kernel for STFT based on linear canonical decomposition. In the development of kernel , Fractional Fourier Transform (FrFT) is used which provides noise free frequency domain representation .With the help of developed transform kernel , the magnitude-wise shift invariance property is verified and timefrequency content is analyzed by plotting spectrogram. Keywords: STFT, FrFT, kernel, Time-Bandwidth product, spectrogram.
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Safdar, Muhammad Farhan, Robert Marek Nowak, and Piotr Pałka. "A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network." Sensors 22, no. 24 (December 7, 2022): 9576. http://dx.doi.org/10.3390/s22249576.
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The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.
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Feng, Sheng, Xiaoqiang Hua, and Xiaoqian Zhu. "Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction." Entropy 22, no. 9 (August 20, 2020): 914. http://dx.doi.org/10.3390/e22090914.
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In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.
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Ding, Congzhang, Yong Jia, Guolong Cui, Chuan Chen, Xiaoling Zhong, and Yong Guo. "Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms." Remote Sensing 13, no. 21 (October 23, 2021): 4264. http://dx.doi.org/10.3390/rs13214264.
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According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. In the designed parallelism LSTM framework, multiple parallel LSTM sub-networks are trained separately to extract different temporal features from the spectrogram of each frequency and produce corresponding classification probabilities. At the decision level, the probabilities of activity classification from these sub-networks are fused by addition as the recognition output. To validate the proposed method, an experimental data set is collected by using an SFCW radar to monitor 11 participants who continuously perform six activities in sequence with three different transitions and random durations. The validation results demonstrate that the average accuracies of the designed parallelism unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) based on five frequency spectrograms are 85.41% and 96.15%, respectively, outperforming traditional Uni-LSTM and Bi-LSTM networks with only a single-frequency spectrogram by 5.35% and 6.33% at least. Additionally, the recognition accuracy of the parallelism LSTM network reveals an upward trend as the number of multi-frequency spectrograms (namely the number of LSTM subnetworks) increases, and tends to be stable when the number reaches 4.
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Burriel-Valencia, Jordi, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Baño, Martin Riera-Guasp, and Manuel Pineda-Sánchez. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines." Energies 12, no. 17 (August 31, 2019): 3361. http://dx.doi.org/10.3390/en12173361.
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Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it—short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.
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Merchan, Fernando, Ariel Guerra, Héctor Poveda, Héctor M. Guzmán, and Javier E. Sanchez-Galan. "Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks." Applied Sciences 10, no. 9 (May 8, 2020): 3286. http://dx.doi.org/10.3390/app10093286.
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We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.
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Park, Dongsuk, Seungeui Lee, SeongUk Park, and Nojun Kwak. "Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks." Sensors 21, no. 1 (December 31, 2020): 210. http://dx.doi.org/10.3390/s21010210.
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With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.
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Chi, Tai-Shih, and Chung-Chien Hsu. "Multiband analysis and synthesis of spectro-temporal modulations of Fourier spectrogram." Journal of the Acoustical Society of America 129, no. 5 (May 2011): EL190—EL196. http://dx.doi.org/10.1121/1.3565471.
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Gao, Zhi Bin. "Short Time Fourier Transform Analysis of Multi-Component Nonstationary Acoustic Signal." Advanced Materials Research 403-408 (November 2011): 3163–65. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3163.
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In order to extract major components of signal, multi-component nonstationary acoustic signal was analyzed with time-frequency analysis technique. By transforming multi-component nonstationary acoustic signal from time domain to time-frequency domain with short time Fourier transform, major components were determined according to spectrogram. Results show that major components and its time-frequency characteristic parameters can be extracted exactly. Short time Fourier transform is an effective method for extracting major components of nonstationary acoustic signal.
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Yegnanarayana, B. "Group delay spectrogram of speech signals without phase wrapping." Journal of the Acoustical Society of America 151, no. 3 (March 2022): 2181–91. http://dx.doi.org/10.1121/10.0009922.
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This paper proposes a method for displaying the phase information in speech signals through group delay spectrogram, without the need for phase unwrapping. The method involves scaling down the phase values without affecting the shape of the phase or group delay function, thus preserving the information of the phase spectrum. This is accomplished using single-frequency filtering (SFF) of speech signals to obtain the instantaneous complex SFF spectrum. The SFF involves filtering a frequency-shifting signal using a resonator at half the sampling frequency. The SFF spectrum displays characteristics similar to the standard short-time Fourier transform (STFT) spectrum, but without the effects of truncation due to windowing operation. The objective of the present study is to show that features of speech production can also be observed in the phase information, displayed through the group delay spectrogram. The time–frequency resolution in the group delay spectrogram depends on the choice of the bandwidth of the resonator used in the SFF analysis. The speech production features displayed in the group delay spectrogram are examined for different types of speech signals at different time-frequency resolutions.
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Debbal, S. M., and F. Bereksi-Reguig. "COMPLEMENTARY ANALYSIS TO HEART SOUNDS WHILE USING THE SHORT TIME FOURIER AND THE CONTINUOUS WAVELET TRANSFORMS." Biomedical Engineering: Applications, Basis and Communications 19, no. 05 (October 2007): 331–39. http://dx.doi.org/10.4015/s1016237207000434.
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This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.
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Mandal, Sunandan, Kavita Thakur, Bikesh Kumar Singh, and Heera Ram. "Performance Evaluation of Spectrogram Based Epilepsy Detection Techniques Using Gray Scale Features." Journal of Ravishankar University (PART-B) 33, no. 1 (July 4, 2020): 01–07. http://dx.doi.org/10.52228/jrub.2020-33-1-1.
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Electroencephalogram (EEG) is most common instrument for treatment and diagnosis of brain related diseases. Analysis of EEG signals for treatment of patient is time consuming and not easy task for neurologist. There is always a chance of human error. The purpose of this paper is to present an automatic detection model for epileptic seizure from EEG signals. To fulfill this objective, EEG signals are preprocessed and converted into spectrogram images using Short Time Fourier Transform (STFT). From this spectrogram images gray scale features are extracted. Support Vector Machine (SVM) with six different kernel functions and three data division protocols are utilized for performance evaluation of proposed model. Results show that quadratic SVM classifier has achieved highest classification accuracy.
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DEBBAL, S. M., and F. BEREKSI-REGUIG. "SECOND CARDIAC SOUND ANALYSIS TECHNIQUES AND PERFORMANCE COMPARISON." Journal of Mechanics in Medicine and Biology 05, no. 03 (September 2005): 429–42. http://dx.doi.org/10.1142/s021951940500162x.
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This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the second cardiac sound S2 of the phonocardiogram signal (PCG). A comparison between these methods has shown the resolution differences between them. It is found that the spectrogram Short-Time Fourier Transform (STFT) cannot detect the two internals components of the second sound S2 (A2 and P2, atrial and pulmonary components respectively). The Wigner Distribution (WD) can provide time-frequency characteristics of the sound S2, but with insufficient diagnostic information as the two components (A2 and P2) are not accurately detected, appearing to be one component only. It is found that the wavelet transform (WT) is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second cardiac sound S2. However, the standard Fourier transform can display these components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the second sound S2 that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.
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Zhang, Feiyu, Luyang Zhang, Hongxiang Chen, and Jiangjian Xie. "Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs." Entropy 23, no. 11 (November 13, 2021): 1507. http://dx.doi.org/10.3390/e23111507.
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Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability.
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Nisar, Shibli, Omar Usman Khan, and Muhammad Tariq. "An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6172453.
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Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input signal is known. In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique. For wide-band signals, where a fixed time-frequency resolution is undesirable, the approach adapts the constant Q transform (CQT). Unlike the STFT, the CQT provides a varying time-frequency resolution. This results in a high spectral resolution at low frequencies and high temporal resolution at high frequencies. In this paper, a simple but effective switching framework is provided between both STFT and CQT. The proposed method also allows for the dynamic construction of a filter bank according to user-defined parameters. This helps in reducing redundant entries in the filter bank. Results obtained from the proposed method not only improve the spectrogram visualization but also reduce the computation cost and achieves 87.71% of the appropriate window length selection.
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Żakowski, Krzysztof, and Kazimierz Darowicki. "Detection of Stray Current Field Interference on Metal Constructions Using STFT." Key Engineering Materials 293-294 (September 2005): 785–0. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.785.
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A method of detection of stray currents using the Short Time Fourier Transformation (STFT) is presented. This particular kind of signal analysis makes the determination of changes of the spectral power density of a signal (e.g. structure to electrolyte potential) in function of time possible. The results of joint time-frequency analysis of the potential in the field of stray currents generated by tram-line are presented. The spectrogram is a composition of spectral lines of defined frequency distribution. A good correlation of localization of spectral lines corresponding to rail potential and pipeline potential in time domain is visible. These peaks may be treated as a characteristic feature of tram passing. The comparison of time and frequency localization of peaks from spectrograms is unambiguous evidence that the electric field generated by passing trams interfered on the investigated pipeline. A presented result unambiguously shows the possibility of accurate identification of source of stray currents and its interference on the metal construction.
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Gusev, V. G. "Double-exposure recording of a lensless fourier spectrogram for forming a shear interferogram." Russian Physics Journal 42, no. 5 (May 1999): 462–66. http://dx.doi.org/10.1007/bf02508218.
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Ferraioli, Luigi, Michele Armano, Heather Audley, Giuseppe Congedo, Ingo Diepholz, Ferran Gibert, Martin Hewitson, et al. "Kolmogorov-Smirnov like test for time-frequency Fourier spectrogram analysis in LISA Pathfinder." Experimental Astronomy 39, no. 1 (December 19, 2014): 1–10. http://dx.doi.org/10.1007/s10686-014-9432-z.
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Guo, Changliang, Duo Fang, Chengzong Wang, Tao Qin, Zenghua Liu, Zehua Liu, and Yu Zhang. "Ultrasonic flaw detection spectrogram characterization of vermicular graphite cast iron engine cylinder head." Journal of Physics: Conference Series 1996, no. 1 (August 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/1996/1/012005.
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Abstract The defects formed in the manufacture of the vermicular graphite cast iron engine cylinder head seriously affect the operation of the engine, which is necessary to detect. Ultrasonic testing is a non-destructive testing method that has the advantages of quick response, high resolution, and high security. In this paper, various types of specimens are prepared corresponding to different types of actual defects in the vermicular iron cylinder head. An ultrasonic A-scan system was built to test the specimens. The short-time Fourier transform, the continuous wavelet transform, the empirical wavelet transform, and the empirical modal decomposition were adopted to transform the signals into spectrograms which were further analyzed to reveal the inherent features of defects. The results show that the short-time Fourier transform can be used to distinguish all the common defects comparing to other methods. Comparing to the time-domain waveforms, the transformed spectrograms provide clear time-frequency distribution and highlight the inherent characteristics of the signal.
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García, Mario Alejandro, and Eduardo Atilio Destéfanis. "The Power Cepstrum Calculation with Convolutional Neural Networks." Journal of Computer Science and Technology 19, no. 2 (October 10, 2019): e13. http://dx.doi.org/10.24215/16666038.19.e13.
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A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.
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Ahmed, Ammar, Youssef Serrestou, Kosai Raoof, and Jean-François Diouris. "Empirical Mode Decomposition-Based Feature Extraction for Environmental Sound Classification." Sensors 22, no. 20 (October 11, 2022): 7717. http://dx.doi.org/10.3390/s22207717.
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In environment sound classification logs, Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In this study, we address these limitations of Fourier transform and propose a new method to extract log Mel band energies using amplitude modulation and frequency modulation. We present a comparative study between traditionally used log Mel band energy features extracted by Fourier transform and log Mel band energy features extracted by our new approach. This approach is based on extracting log Mel band energies from estimation of instantaneous frequency (IF) and instantaneous amplitude (IA), which are used to construct a spectrogram. The estimation of IA and IF is made by associating empirical mode decomposition (EMD) with the Teager–Kaiser energy operator (TKEO) and the discrete energy separation algorithm. Later, a Mel filter bank is applied to the estimated spectrogram to generate EMD-TKEO-based MBEs, or simply, EMD-MBEs. In addition, we employ the EMD method to remove signal trends from the original signal and generate another type of MBE, called S-MBEs, using FFT and a Mel filter bank. Four different datasets were utilised and convolutional neural networks (CNN) were trained using features extracted from Fourier transform-based MBEs (FFT-MBEs), EMD-MBEs, and S-MBEs. In addition, CNNs were trained with an aggregation of all three feature extraction techniques and a combination of FFT-MBEs and EMD-MBEs. Individually, FFT-MBEs achieved higher accuracy compared to EMD-MBEs and S-MBEs. In general, the system trained with the combination of all three features performed slightly better compared to the system trained with the three features separately.
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Qi, Xin. "Synthesis and Characterization of Strong Polar Macroporous Resin Made from Cellulose." Advanced Materials Research 346 (September 2011): 743–50. http://dx.doi.org/10.4028/www.scientific.net/amr.346.743.
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The macroporous adsorption resin synthesized in this experiment uses cullulose as monomer, adipoyl dichlorid as crosslinker, and cyclohexane as porogenic agent, which three corsslink and polymerize each other, forming the porous skeletal structure. The cellulose processing procedure is as follow: prepare alkali cellulose; crosslink the cellulose (etherification); etherify the cellulose; and functionalize the cellulose. By assaying the perssad characterization of the macroporous resin obtained in this experiment with Fourier infrared spectrometer, we observe hydroxyl group and ester group in the spectrogram. Observing the spectrogram, we find hydroxyl group, indicating that the hydroxyl group in the cellulose has not reacted fully, while the ester group in the spectrogram showing that the ester group has reacted fully with the cellulose. After the aperture characterization for the produced resin with scanning electron microscope (SEM), we find there are unevenly distributed apertures on different levels, which means that new macroporous resin has been synthesized. This paper taking rutin as the adsorbate explores the propertis of static adsorption and desorption of the synthetic macroporous resin, and the influence on its adsorption capability under different situations. The adsorption data shows that the adsorption of rutin on the very resin conforms to the Freundlich isothermal adsorption equation.
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Mi, Dan, and Lu Qin. "Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2047576.
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National music is a treasure of Chinese traditional culture. It contains the cultural characteristics of various regions and reflects the core value of Chinese traditional culture. Classification technology classifies a large number of unorganized drama documents, which are not labeled, and to some extent, it helps folk music better enter the lives of ordinary people. Simulate folk music of different spectrum and record corresponding music audio under laboratory conditions Through Fourier transform and other methods, music audio is converted into spectrogram, and a total of 2608 two-dimensional spectrogram images are obtained as datasets. The sonogram dataset is imported into the deep convolution neural network GoogLeNet for music type recognition, and the test accuracy is 99.6%. In addition, the parallel GoogLeNet technology based on inverse autoregressive flow is used. The unique improvement is that acoustic features can be quickly converted into corresponding speech time-domain waveforms, reaching the real-time level, improving the efficiency of model training and loading, and outputting speech with higher naturalness. In order to further prove the reliability of the experimental results, the spectrogram datasets are imported into Resnet18 and Shufflenet for training, and the test accuracy of 99.2% is obtained. The results show that this method can effectively classify and recognize music. The experimental results show that this scheme can achieve more accurate classification. The research realizes the recognition of national music through deep learning spectrogram classification for the first time, which is an intelligent and fast new method of classification and recognition.
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Hendriks, Jacob, and Patrick Dumond. "Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs." Vibration 4, no. 2 (April 3, 2021): 284–309. http://dx.doi.org/10.3390/vibration4020019.
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This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional kernel sizes and stride. The results show that each input type favors different combinations of hyperparameters, and that each of the datasets studied yield different performance characteristics. The input sizes are found to be the most significant determiner of whether overfitting will occur. It is demonstrated that CNNs trained with spectrograms are less dependent on hyperparameter optimization over all three datasets. This paper demonstrates the wide range of performance achieved by CNNs when preprocessing method and hyperparameters are varied as well as their complex interaction, providing researchers with useful background information and a starting place for further optimization.
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Ma, Longbo, Jianying Zheng, and Jianliang Zhao. "Flow fields disturbance research of regulating valve based on short time fourier transform spectrogram." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 2009, no. 8 (December 16, 2009): 72–77. http://dx.doi.org/10.3724/sp.j.1187.2009.08072.
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Lilensten, J., and P. O. Amblard. "Time-frequency tools of signal processing for EISCAT data analysis." Annales Geophysicae 14, no. 12 (December 31, 1996): 1513–25. http://dx.doi.org/10.1007/s00585-996-1513-5.
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Abstract. We demonstrate the usefulness of some signal-processing tools for the EISCAT data analysis. These tools are somewhat less classical than the familiar periodogram, squared modulus of the Fourier transform, and therefore not as commonly used in our community. The first is a stationary analysis, "Thomson's estimate'' of the power spectrum. The other two belong to time-frequency analysis: the short-time Fourier transform with the spectrogram, and the wavelet analysis via the scalogram. Because of the highly non-stationary character of our geophysical signals, the latter two tools are better suited for this analysis. Their results are compared with both a synthetic signal and EISCAT ion-velocity measurements. We show that they help to discriminate patterns such as gravity waves from noise.
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Li, Hongzu, and Pierre Boulanger. "Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features." Sensors 22, no. 7 (March 23, 2022): 2467. http://dx.doi.org/10.3390/s22072467.
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Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients’ heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
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Kristian, Yosi, Natanael Simogiarto, Mahendra Tri Arif Sampurna, and Elizeus Hanindito. "Ensemble of multimodal deep learning autoencoder for infant cry and pain detection." F1000Research 11 (March 28, 2022): 359. http://dx.doi.org/10.12688/f1000research.73108.1.
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Background: Babies cannot communicate their pain properly. Several pain scores are developed, but they are subjective and have high variability inter-observer agreement. The aim of this study was to construct models that use both facial expression and infant voice in classifying pain levels and cry detection. Methods: The study included a total of 23 infants below 12-months who were treated at Dr Soetomo General Hospital. The the Face Leg Activity Cry and Consolability (FLACC) pain scale and recordings of the baby's cries were taken in the video format. A machine-learning-based system was created to detect infant cries and pain levels. Spectrograms with the Short-Time Fourier Transform were used to convert the audio data into a time-frequency representation. Facial features combined with voice features extracted by using the Deep Learning Autoencoders was used for the classification of infant pain levels. Two types of autoencoders: Convolutional Autoencoder and Variational Autoencoder were used for both faces and voices. Result: The goal of the autoencoder was to produce a latent-vector with much smaller dimensions that was still able to recreate the data with minor losses. From the latent-vectors, a multimodal data representation for Convolutional Neural Network (CNN) was used for producing a relatively high F1 score, higher than single data modal such as the voice or facial expressions alone. Two major parts of the experiment were: 1. Building the three autoencoder models, which were autoencoder for the infant’s face, amplitude spectrogram, and dB-scaled spectrogram of infant’s voices. 2. Utilising the latent-vector result from the autoencoders to build the cry detection and pain classification models. Conclusion: In this paper, four pain classifier models with a relatively good F1 score was developed. These models were combined by using ensemble methods to improve performance, which resulted in a better F1 score.
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Kristian, Yosi, Natanael Simogiarto, Mahendra Tri Arif Sampurna, Elizeus Hanindito, and Visuddho Visuddho. "Ensemble of multimodal deep learning autoencoder for infant cry and pain detection." F1000Research 11 (January 30, 2023): 359. http://dx.doi.org/10.12688/f1000research.73108.2.
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Background: Babies cannot communicate their pain properly. Several pain scores are developed, but they are subjective and have high variability inter-observer agreement. The aim of this study was to construct models that use both facial expression and infant voice in classifying pain levels and cry detection. Methods: The study included a total of 23 infants below 12-months who were treated at Dr Soetomo General Hospital. The the Face Leg Activity Cry and Consolability (FLACC) pain scale and recordings of the baby's cries were taken in the video format. A machine-learning-based system was created to detect infant cries and pain levels. Spectrograms with the Short-Time Fourier Transform were used to convert the audio data into a time-frequency representation. Facial features combined with voice features extracted by using the Deep Learning Autoencoders was used for the classification of infant pain levels. Two types of autoencoders: Convolutional Autoencoder and Variational Autoencoder were used for both faces and voices. Result: The goal of the autoencoder was to produce a latent-vector with much smaller dimensions that was still able to recreate the data with minor losses. From the latent-vectors, a multimodal data representation for Convolutional Neural Network (CNN) was used for producing a relatively high F1 score, higher than single data modal such as the voice or facial expressions alone. Two major parts of the experiment were: 1. Building the three autoencoder models, which were autoencoder for the infant’s face, amplitude spectrogram, and dB-scaled spectrogram of infant’s voices. 2. Utilising the latent-vector result from the autoencoders to build the cry detection and pain classification models. Conclusion: In this paper, four pain classifier models with a relatively good F1 score was developed. These models were combined by using ensemble methods to improve performance, which resulted in a better F1 score.
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Geng, Lei, Hongfeng Shan, Zhitao Xiao, Wei Wang, and Mei Wei. "Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method." Biomedical Engineering / Biomedizinische Technik 66, no. 6 (November 29, 2021): 613–25. http://dx.doi.org/10.1515/bmt-2021-0112.
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Abstract Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal’s harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.
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Manzanares-Martinez, Jesus, Carlos Ivan Ham-Rodriguez, and Betsabe Manzanares-Martinez. "Recovery of transit times and frequencies of multiple pulses via the Short-Time Fourier Transform." Revista Mexicana de Física 64, no. 3 (April 30, 2018): 296. http://dx.doi.org/10.31349/revmexfis.64.296.
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In this work, we present a study to determine the transit times and frequencies of pulses by usingthe Short-Time Fourier Transform (STFT). We consider the case of an acoustic signal composed offive Gaussian pulses which have a high overlapping in time but oscillate at different frequencies. Weproceeded in three steps. First, we illustrate how the STFT calculated through a sliding windowproduces a spectrogram where transit time is on one axis and frequency on the other. Second, wederive an exact analytical solution of the STFT to develop an intuitive vision of the mathematicaltechnique. Finally, in a third step, we present an experiment to demonstrate that the STFT is auseful technique to characterize a complex acoustical signal.
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Torsvik, T., T. Soomere, I. Didenkulova, and A. Sheremet. "Identification of ship wake structures by a time–frequency method." Journal of Fluid Mechanics 765 (January 19, 2015): 229–51. http://dx.doi.org/10.1017/jfm.2014.734.
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AbstractThe wake of a ship that sails at relatively large Froude numbers usually contains a number of components of different nature and with different heights, lengths, timings and propagation directions. We explore the possibilities of the spectrogram representation of one-point measurements of the ship wake to identify these components and to quantify their main properties. This representation, based on the short-time Fourier transform, facilitates a reliable decomposition of the wake into constituent components and makes it possible to quantify their variations in the time–space domain and the energy content of each component, from very low-frequency precursor waves up to high-frequency signals within the frequency range of typical wind-generated waves. A method for estimation of the ship speed and the distance of its sailing line from the measurement site is proposed, which only uses information available within the record of the ship wake surface elevation, but where it is assumed that the wake pattern does not deviate significantly from the classical Kelvin wake structure. The wake decomposition using the spectrogram method allows investigation of the energy content that can be attributed to each individual component of the wake. We demonstrate that the majority (60–80 %) of wake energy from strongly powered large ferries that sail at depth Froude numbers ${\sim}0.7$ is concentrated in components that are located near the edge of the wake wedge. Finally, we demonstrate that the spectrogram representation offers a convenient way to identify a specific signature of single types of ships.
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Noak, Devin Reness, and I. Dewa Made Bayu Atmaja Darmawan. "Real Time Pitch Detection For Vocal Tuning Using FFT Algoritma And Spectrogram." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 3 (January 25, 2020): 325. http://dx.doi.org/10.24843/jlk.2020.v08.i03.p15.
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The song is a means of entertainment most often heard by humans where in the song consists of music and vocals. Good quality music and vocal singers will make a song more pleasant to hear. To make the song sound tunable and in accordance with the rhythm can be done by adjusting the vocals according to the tone of the song. From this we know that measuring sound frequencies needs to be done to determine whether a frequency or period is loud, it can also be used as a tool in vocal training, one of them for vocal tuning applications to find the harmonious sound of the sound. Moreover, it can be used as a learning need in Sound Frequency Processing. Where one of the parts to create a vocal tuning application can be through the Real-time spectrogram program. This RTS uses Pyaudio as sound recording, uses the Python 3.6 programming language and uses the Fast Fourier Transform method which will help when making real-time spectrogram and pitch detection programs. The test results obtained 75% accuracy in real-time pitch detection programs.
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Hornsteiner, C., and J. Detlefsen. "Characterisation of human gait using a continuous-wave radar at 24 GHz." Advances in Radio Science 6 (May 26, 2008): 67–70. http://dx.doi.org/10.5194/ars-6-67-2008.
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Abstract. Human locomotion consists of a complex movement of various parts of the body. The reflections generated by body parts with different relative velocities result in different Doppler shifts which can be detected as a superposition with a Continuous-Wave (CW) Radar. A time-frequency transform like the short-time Fourier transform (STFT) of the radar signal allows a representation of the signal in both time- and frequency domain (spectrogram). It can be shown that even during one gait cycle the velocity of the torso, which constitutes the major part of the reflection, is not constant. Further a smaller portion of the signal is reflected from the legs. The velocity of the legs varies in a wide range from zero (foot is on the ground) to a velocity which is higher than that of the torso. The two dominant parameters which characterise the human gait are the step rate and the mean velocity. Both parameters can be deduced from suitable portions of the spectrogram. The statistical evaluation of the two parameters has the potential to be included for discrimination purposes either between different persons or between humans and other moving objects.
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Jia, Dandi, Qiang Gao, and Hui Deng. "Stock Market Prediction Based on Time-frequency Analysis and Convolutional Neural Network." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2224/1/012017.
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Abstract Recently, researchers have shown an increased interest in stock market prediction with neural networks. Stock market is affected by a multiplicity of factors with different active periods, thus financial time series possess multiscale frequency characteristics, which can be exploited to facilitate prediction of stock market. In this paper, we propose a stock market prediction model combining time-frequency analysis and convolutional neural network (CNN), in which the influence extent of different frequency components has been considered. We transform original financial time series into the spectrogram reflecting time-localized frequency information by short-time Fourier transform (STFT). The 2-dimensional time-frequency feature is obtained from the spectrogram by frequency bands extraction, which is then pre-weighted and input into CNN to forecast the future price change. The frequency bands extraction and pre-weight are set according to the frequency influence. The results of experiments on Shanghai Composite Index show that the proposed model with frequency bands extraction considering frequency influence achieves a 4% relative decrease in mean absolute error (MAE) compared with that does not consider the frequency influence. Moreover, the pre-weight gives an additional 3% relative decrease of MAE.
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Zhang, Yujin, Shuxian Dai, Wanqing Song, Lijun Zhang, and Dongmei Li. "Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images." Electronics 9, no. 1 (December 25, 2019): 23. http://dx.doi.org/10.3390/electronics9010023.
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Speech tampering may be aided by the resampling operation. It is significant for speech forensics to effectively detect the resampling; however, there are few studies on speech resampling detection. The purpose of this paper was therefore to provide a new training ideal to detect speech resampling. After resampling, the speech signal changes regularly in the time–frequency domain. In this paper, we theoretically analyzed the corresponding relationship between time domain and frequency domain of the resampled speech. Compared with the original speech, the bandwidth of resampled speech was stretched or compressed. First, the spectrogram was generated by short-time Fourier transform (STFT) from the speech. Then, the local binary pattern (LBP) operator was applied to model the statistical changes in the spectrogram and the LBP histogram was calculated as discriminative features. Finally, a support vector machine (SVM) was applied to classify the developed features to identify whether the speech had undergone the resampling operation. The experimental results show that the proposed method has superior detection performance in different resampling scenarios than some existing methods, and the proposed features are very robust against the commonly used compression post-processing operation. This highlights the promising potential of the proposed method as a speech resampling detection tool in practical forensics applications.
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Ozcelik, Salih T. A., Hakan Uyanık, Erkan Deniz, and Abdulkadir Sengur. "Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals." Diagnostics 13, no. 2 (January 4, 2023): 182. http://dx.doi.org/10.3390/diagnostics13020182.
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Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the “silent killer” reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.
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Kaplun, Dmitry, Alexander Voznesensky, Sergei Romanov, Valery Andreev, and Denis Butusov. "Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System." Applied Sciences 10, no. 9 (April 29, 2020): 3097. http://dx.doi.org/10.3390/app10093097.
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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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Papadimitriou, Ioannis, Anastasios Vafeiadis, Antonios Lalas, Konstantinos Votis, and Dimitrios Tzovaras. "Audio-Based Event Detection at Different SNR Settings Using Two-Dimensional Spectrogram Magnitude Representations." Electronics 9, no. 10 (September 29, 2020): 1593. http://dx.doi.org/10.3390/electronics9101593.
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Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the multimodal approaches are to become better in a range of fields of interest, audio analysis will have to play an integral part. Event recognition in autonomous vehicles (AVs) is such a field at a nascent stage that can especially leverage solely on audio or can be part of the multimodal approach. In this manuscript, an extensive analysis focused on the comparison of different magnitude representations of the raw audio is presented. The data on which the analysis is carried out is part of the publicly available MIVIA Audio Events dataset. Single channel Short-Time Fourier Transform (STFT), mel-scale and Mel-Frequency Cepstral Coefficients (MFCCs) spectrogram representations are used. Furthermore, aggregation methods of the aforementioned spectrogram representations are examined; the feature concatenation compared to the stacking of features as separate channels. The effect of the SNR on recognition accuracy and the generalization of the proposed methods on datasets that were both seen and not seen during training are studied and reported.
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Swiercz, Ewa. "Time-frequency tomographic imaging of a rotating object in a narrow-band radar." International Journal of Microwave and Wireless Technologies 8, no. 6 (April 15, 2016): 871–79. http://dx.doi.org/10.1017/s1759078716000404.
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The backscatter from radar object carries Doppler information of scatterers on the object determined by the radial velocity of scattering points and the radar transmitted frequency. For a rotating object this information is contained in the frequency characteristics over varying aspect angle. Frequency characteristics are used to create projections for Doppler radar tomographic imaging. This paper presents a method for high resolution imaging of a rotating target using a time-frequency transform of a returned signal as tomographic projections. The resolution of a tomographic image depends not only on radar system parameters but also depends on the resolution of input projections. The reassigned spectrogram is proposed for building of tomographic projections, due to its possibility of squeezing of frequency spread. The reassigned spectrogram is sensitive to noise so the denoising procedure in the time-frequency domain must be performed before the reassignment procedure. The denoising is performed by removing Short Time Fourier Transform (STFT) noise coefficients below the appropriate threshold. The STFT is a linear time-frequency transform and coefficients, which belong to the signal and coefficients which belong to noise can be analyzed separately. The efficiency of the proposed idea of imaging is supported by results of numerical experiments.
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Leonardi, Mauro, Gianluca Ligresti, and Emilio Piracci. "Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis." Drones 6, no. 5 (May 11, 2022): 124. http://dx.doi.org/10.3390/drones6050124.
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The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present.
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Liu, Quan, Li Ma, Shou-Zen Fan, Maysam F. Abbod, Qingsong Ai, Kun Chen, and Jiann-Shing Shieh. "Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform." BioMed Research International 2018 (July 15, 2018): 1–16. http://dx.doi.org/10.1155/2018/4939480.
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Electroencephalogram (EEG) signal analysis is commonly employed to extract information on the brain dynamics. It mainly targets brain status and communication, thus providing potential to trace differences in the brain’s activity under different anesthetics. In this article, two kinds of gamma-amino butyric acid (type A -GABAA) dependent anesthetic agents, propofol and desflurane (28 and 23 patients), were studied and compared with respect to EEG spectrogram dynamics. Hilbert-Huang Transform (HHT) was employed to compute the time varying spectrum for different anesthetic levels in comparison with Fourier based method. Results show that the HHT method generates consistent band power (slow and alpha) dominance pattern as Fourier method does, but exhibits higher concentrated power distribution within each frequency band than the Fourier method during both drugs induced unconsciousness. HHT also finds slow and theta bands peak frequency with better convergence by standard deviation (propofol-slow: 0.46 to 0.24; theta: 1.42 to 0.79; desflurane-slow: 0.30 to 0.25; theta: 1.42 to 0.98) and a shift to relatively lower values for alpha band (propofol: 9.94 Hz to 10.33 Hz, desflurane 8.44 Hz to 8.84 Hz) than Fourier one. For different stage comparisons, although HHT shows significant alpha power increases during unconsciousness stage as the Fourier did previously, it finds no significant high frequency (low gamma) band power difference in propofol whereas it does in desflurane. In addition, when comparing the HHT results within two groups during unconsciousness, high beta band power in propofol is significantly larger than that of desflurane while delta band power behaves oppositely. In conclusion, this study convincingly shows that EEG analyzed here considerably differs between the HHT and Fourier method.