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Journal articles on the topic 'Spectrograms'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

He, Yuan, Xinyu Li, Runlong Li, Jianping Wang, and Xiaojun Jing. "A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration." Sensors 20, no. 17 (2020): 5007. http://dx.doi.org/10.3390/s20175007.

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Radio frequency interference, which makes it difficult to produce high-quality radar spectrograms, is a major issue for micro-Doppler-based human activity recognition (HAR). In this paper, we propose a deep-learning-based method to detect and cut out the interference in spectrograms. Then, we restore the spectrograms in the cut-out region. First, a fully convolutional neural network (FCN) is employed to detect and remove the interference. Then, a coarse-to-fine generative adversarial network (GAN) is proposed to restore the part of the spectrogram that is affected by the interferences. The sim

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Li, Juan, Xueying Zhang, Lixia Huang, Fenglian Li, Shufei Duan, and Ying Sun. "Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network." Applied Sciences 12, no. 19 (2022): 9518. http://dx.doi.org/10.3390/app12199518.

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In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the fir

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Johnson, Alexander. "An integrated approach for teaching speech spectrogram analysis to engineering students." Journal of the Acoustical Society of America 152, no. 3 (2022): 1962–69. http://dx.doi.org/10.1121/10.0014172.

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Spectrogram analysis is a vital skill for learning speech acoustics. Spectrograms are necessary for visualizing cause-effect relationships between speech articulator movements and the resulting sound produced. However, many interpretation techniques needed to read spectrograms are counterintuitive to engineering students who have been taught to use more rigid mathematical formulas. As a result, spectrogram reading is often challenging for these students who do not have prior background in acoustic phonetics. In this paper, a structured, inclusive framework for teaching spectrogram reading to s

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Kim, Seong-Yoon, Hyun-Min Lee, Chae-Young Lim, and Hyun-Woo Kim. "Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning." Applied Sciences 15, no. 9 (2025): 4679. https://doi.org/10.3390/app15094679.

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Acoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep learning model. Spectrograms effectively capture the temporal and frequency characteristics of acoustic signals. In this work, we extract key features such as spectrograms, Mel-spectrograms, and MFCCs from raw acoustic data and use them as input for training a convolutional neural network. The proposed model is based on a custom ResNet architecture

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Basak, Gopal K., and Tridibesh Dutta. "Statistical Speaker Identification Based on Spectrogram Imaging." Calcutta Statistical Association Bulletin 59, no. 3-4 (2007): 253–63. http://dx.doi.org/10.1177/0008068320070309.

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Abstract: The paper addresses the problem of speaker identification based on spectrograms in the text dependent case. Using spectrogram segmentation, this paper, mainly, focusses on understanding the complex patterns in frequency and amplitude in an utterance of a given word by an individual. The features used for identifying a speaker based on an observed variable extracted from the spectrograms, rely on the distinct speaker effect, his/her interaction effect with the particular word and with the frequency bands of the spectrogram. Performance of this novel approach on spectrogram samples, co

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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 s

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Jiashen, Li, and Zhang Xianwu. "Extracting speech spectrogram of speech signal based on generalized S-transform." PLOS ONE 20, no. 1 (2025): e0317362. https://doi.org/10.1371/journal.pone.0317362.

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In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions. The flexibility of an algorithm in providing different resolutions is crucial for these applications. This paper introduces the generalized S-transform, and explains its fundamental theory and a

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Franzoni, Valentina. "Cross-domain synergy: Leveraging image processing techniques for enhanced sound classification through spectrogram analysis using CNNs." Journal of Autonomous Intelligence 6, no. 3 (2023): 678. http://dx.doi.org/10.32629/jai.v6i3.678.

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<p>In this paper, the innovative approach to sound classification by exploiting the potential of image processing techniques applied to spectrogram representations of audio signals is reviewed. This study shows the effectiveness of incorporating well-established image processing methodologies, such as filtering, segmentation, and pattern recognition, to enhance the feature extraction and classification performance of audio signals when transformed into spectrograms. An overview is provided of the mathematical methods shared by both image and spectrogram-based audio processing, focusing o

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Griffin, R. F., та R. E. M. Griffin. "The BE II λ 3130 Å Region in the Spectra of Vega and Sirius". Symposium - International Astronomical Union 111 (1985): 439. http://dx.doi.org/10.1017/s007418090007916x.

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Spectrograms of very high quality have been obtained of Vega and Sirius with the Mount Wilson 100-inch (2.5-m) telescope and coudé spectrograph. Examples of these plates, showing the Be II region in the ultraviolet, are exhibited. The reciprocal dispersion is 0.83 Å/mm (83 nm/m) and the FWHM is about 19 mÅ (1.9 pm). The spectrograms have a trailed width of 3 mm and are on IIa or IIIa emulsions.

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Shingchern D. You, Kai-Rong Lin, and Chien-Hung Liu. "Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling." International Journal of Engineering and Technology Innovation 13, no. 4 (2023): 313–27. http://dx.doi.org/10.46604/ijeti.2023.11975.

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This paper proposes an approach called block scaling quality (BSQ) for estimating the prediction accuracy of a deep network model. The basic operation perturbs the input spectrogram by multiplying all values within a block by , where is equal to 0 in the experiments. The ratio of perturbed spectrograms that have different prediction labels than the original spectrogram to the total number of perturbed spectrograms indicates how much of the spectrogram is crucial for the prediction. Thus, this ratio is inversely correlated with the accuracy of the dataset. The BSQ approach demonstrates satisfac

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Lv, Dan, Yan Zhang, Danjv Lv, Jing Lu, Yixing Fu, and Zhun Li. "Combining CBAM and Iterative Shrinkage-Thresholding Algorithm for Compressive Sensing of Bird Images." Applied Sciences 14, no. 19 (2024): 8680. http://dx.doi.org/10.3390/app14198680.

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Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand the health of ecosystems. However, bird images and audio involve a vast amount of data. To improve the efficiency of data transmission and storage efficiency and save bandwidth, compressive sensing can overcome this challenge. Compressive sensing is a technique that uses the sparsity of signals to recover original data from a small number of linear m

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Kwon, Daehyun, Hanbit Kang, Dongwoo Lee, and Yoon-Chul Kim. "Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram." PLOS ONE 20, no. 3 (2025): e0317630. https://doi.org/10.1371/journal.pone.0317630.

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Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for monitoring heart rhythms and detecting cardiac arrhythmias or other heart conditions. The integration of ECG signal visualization with AI-based abnormality detection empowers users to independently and confidently assess their physiological signals. In this study, we investigated a novel method for visualizing ECG signals using polar transformations of short-time Fourier transform (STFT) spectrograms and evaluated the performance of deep convolutional neural networks (CNNs) in predicting atrial fi

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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 (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 wit

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Yang, Guang, Kainan Guan, Jiarun Yang, Li Zou, and Xinhua Yang. "Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network." Electronics 12, no. 24 (2023): 4910. http://dx.doi.org/10.3390/electronics12244910.

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The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inc

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Ladefoged, Peter. "Answers to spectrograms in 20.1." Journal of the International Phonetic Association 20, no. 2 (1990): 19–20. http://dx.doi.org/10.1017/s0025100300004199.

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In the previous issue of the Journal, on page 52, there were reproductions of two spectrograms that readers were invited to interpret. Both had been tested by presenting them to a group of about half a dozen experienced spectrogram readers. This group discussed the reproductions, arguing for different interpretations until they had reached a consensus on what phrases must have been spoken. The following are some comments that readers might wish to consider, based on the group's procedure in interpreting the first phrase, which is reproduced again below. Remember that the spectrograms contain E

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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 (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 pr

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Jenkins, William F., Peter Gerstoft, Chih-Chieh Chien, and Emma Ozanich. "Reducing dimensionality of spectrograms using convolutional autoencoders." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A178. http://dx.doi.org/10.1121/10.0018582.

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Under the “curse of dimensionality,” distance-based algorithms, such as k-means or Gaussian mixture model clustering, can lose meaning and interpretability in high-dimensional space. Acoustic data, specifically spectrograms, are subject to such limitations due to their high dimensionality: for example, a spectrogram with 100 time- and 100 frequency-bins contains 104 pixels, and its vectorized form constitutes a point in 104-dimensional space. In this talk, we look at four papers that used autoencoding convolutional neural networks to extract salient features of real data. The convolutional aut

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Horn, Skyler, and Hynek Boril. "Gender classification from speech using convolutional networks augmented with synthetic spectrograms." Journal of the Acoustical Society of America 150, no. 4 (2021): A358. http://dx.doi.org/10.1121/10.0008585.

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Automatic gender classification from speech is an integral component of human-computer interfaces. Gender information is utilized in user authentication, speech recognizers, or human-centered intelligent agents. This study focuses on gender classification from speech spectrograms using AlexNet-inspired 2D convolutional neural networks (CNN) trained on real samples augmented with synthetic spectrograms. A generative adversarial network (GAN) is trained to produce synthetic male/female-like speech spectrograms. In limited training data experiments on LibriSpeech, augmenting a training set of 200

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China Venkateswarlu, Guide: Dr S. "Speech Enhancement Using Spectrogram Denoising with Deep U-Net Architectures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48929.

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Abstract -- Acoustic noise significantly degrades speech quality and intelligibility in almost all applications, ranging from telecommunications to voice assistants. In this paper, we address this problem by designing an efficient speech enhancement system based on deep learning. Our approach relies on spectrogram denoising, wherein audio signals are represented as 2D magnitude spectrograms that well maintain signal structure and enable direct application of Convolutional Neural Networks (CNNs). The backbone of our system is a U-Net model, which is a strong deep convolutional autoencoder capab

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Zbezhkhovska, U. R. "DEEPFAKE AUDIO DETECTION USING YOLOV8 WITH MEL-SPECTROGRAM ANALYSIS: A CROSS-DATASET EVALUATION." Radio Electronics, Computer Science, Control, no. 1 (April 10, 2025): 153–63. https://doi.org/10.15588/1607-3274-2025-1-14.

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Context. The problem of detecting deepfake audio has become increasingly critical with the rapid advancement of voice synthesis technologies and their potential for misuse. Traditional audio processing methods face significant challenges in distinguishing sophisticated deepfakes, particularly when tested across different types of audio manipulations and datasets. The object of study isdeveloping a deepfake audio detection model that leverages mel-spectrograms as input to computer vision techniques, focusing on improving cross-dataset generalization capabilities.Objective. The goal of the work

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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 (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

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Yu, Youxin, Wenbo Zhu, Xiaoli Ma, et al. "Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features." Animals 14, no. 22 (2024): 3267. http://dx.doi.org/10.3390/ani14223267.

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In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted t

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Et. al., D. N. V. S. L. S. Indira,. "An Enhanced CNN-2D for Audio-Visual Emotion Recognition (AVER) Using ADAM Optimizer." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1378–88. http://dx.doi.org/10.17762/turcomat.v12i5.2030.

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The importance of integrating visual components into the speech recognition process for improving robustness has been identified by recent developments in audio visual emotion recognition (AVER). Visual characteristics have a strong potential to boost the accuracy of current techniques for speech recognition and have become increasingly important when modelling speech recognizers. CNN is very good to work with images. An audio file can be converted into image file like a spectrogram with good frequency to extract hidden knowledge. This paper provides a method for emotional expression recogniti

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Okazaki, Akira, Yasuhisa Nakamura, and Jun-Ichi Katahira. "Call H and K Emission in the Secondary Component of U Cephei." International Astronomical Union Colloquium 108 (1988): 219–20. http://dx.doi.org/10.1017/s0252921100093878.

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U Cephei (V = 6.8–9.0, P = 2.493 d) is an eclipsing binary consisting of a B7V primary and a G8III-IV secondary component. This binary is one of the semidetached Algol systems showing soft X-ray emission which is probably associated with a hot corona surrounding the secondary component (White and Marshall 1983).We made spectroscopic observations of U Cep with the coudé image-tube spectrograph of the 1.9-m telescope at Okayama Astrophysical Observatory on October 14, 1986. We obtained four spectrograms with a dispersion of 16 Å mm-1 covering λ λ3700—4300 Å during the primary eclipse. The first

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Ferreira, Diogo R., Tiago A. Martins, and Paulo Rodrigues. "Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion." Machine Learning: Science and Technology 3, no. 1 (2021): 015015. http://dx.doi.org/10.1088/2632-2153/ac44aa.

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Abstract In the nuclear fusion community, there are many specialized techniques to analyze the data coming from a variety of diagnostics. One of such techniques is the use of spectrograms to analyze the magnetohydrodynamic (MHD) behavior of fusion plasmas. Physicists look at the spectrogram to identify the oscillation modes of the plasma, and to study instabilities that may lead to plasma disruptions. One of the major causes of disruptions occurs when an oscillation mode interacts with the wall, stops rotating, and becomes a locked mode. In this work, we use deep learning to predict the occurr

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Escapa, I., Daniel Babnigg, Ha Do та ін. "Digitization and Evaluation of Spectrograms from ϵ Aurigae’s 1928–1930 Eclipse". Publications of the Astronomical Society of the Pacific 137, № 1 (2025): 014202. https://doi.org/10.1088/1538-3873/adaa1a.

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Abstract We have digitized 24 spectrograms from the 1928 to 1930 eclipse of ϵ Aurigae, a binary system that has generated extensive investigation due to its long 27.1 yr period and enigmatic secondary, which is completely obscured. The spectrograms that we have digitized comprise the earliest campaign undertaken by the Yerkes Observatory astronomers to record an eclipse of ϵ Aurigae. In this article, we describe our methods of digitization and compare our data with more contemporary spectrogram digitizations. This work demonstrates the quality of scientific data extracted from historical analo

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Rees, Catherine J., P. David Blalock, Shannon E. Kemp, Stacey L. Halum, and Jamie A. Koufman. "Differentiation of adductor-type spasmodic dysphonia from muscle tension dysphonia by spectral analysis." Otolaryngology–Head and Neck Surgery 137, no. 4 (2007): 576–81. http://dx.doi.org/10.1016/j.otohns.2007.03.040.

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Objectives To determine the utility of spectral analysis in the differentiation of adductor-type spasmodic dysphonia (AdSD) from muscle tension dysphonia (MTD). Study Design Prospective blinded study. Methods Forty-seven samples of AdSD-connected speech spectrograms from 27 subjects and 17 samples of MTD-connected speech spectrograms from 15 subjects were selected from clinical charts and de-identified. These spectrograms were reviewed independently and blindly by two speech language pathologists experienced in spectrography. The speech language pathologists designated the spectrogram as consi

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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 (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.

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Appenzeller, I., B. Wolf, and O. Stahl. "An extended nebulosity surrounding the S Dor variable R 127." Symposium - International Astronomical Union 122 (1987): 429–30. http://dx.doi.org/10.1017/s0074180900156876.

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Using the CASPEC echelle spectrograph of the European Southern Observatory, La Silla, Chile, we obtained new high resolution spectrograms of the LMC S Dor variable R 127 in the blue and red spectral range.The red spectrogram, which contains the [N II] 6548 and 6533 and the [S II] 6717 and 6731 lines shows the presence of a well resolved extended gaseous nebula around R 127 (see Figures 1 and 2). The nebula (which is also detected at the Balmer lines) shows blueshifted and redshifted emission (projected) on the position of the stellar continuum, and no wavelength-shift at the maximum (East-West

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Svetlakov, Mikhail, Ilya Kovalev, Anton Konev, Evgeny Kostyuchenko, and Artur Mitsel. "Representation Learning for EEG-Based Biometrics Using Hilbert–Huang Transform." Computers 11, no. 3 (2022): 47. http://dx.doi.org/10.3390/computers11030047.

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A promising approach to overcome the various shortcomings of password systems is the use of biometric authentication, in particular the use of electroencephalogram (EEG) data. In this paper, we propose a subject-independent learning method for EEG-based biometrics using Hilbert spectrograms of the data. The proposed neural network architecture treats the spectrogram as a collection of one-dimensional series and applies one-dimensional dilated convolutions over them, and a multi-similarity loss was used as the loss function for subject-independent learning. The architecture was tested on the pu

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Han, Ying, Qiao Wang, Jianping Huang, et al. "Frequency Extraction of Global Constant Frequency Electromagnetic Disturbances from Electric Field VLF Data on CSES." Remote Sensing 15, no. 8 (2023): 2057. http://dx.doi.org/10.3390/rs15082057.

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The electromagnetic data observed with the CSES (China Seismo-Electromagnetic Satellite, also known as Zhangheng-1 satellite) contain numerous spatial disturbances. These disturbances exhibit various shapes on the spectrogram, and constant frequency electromagnetic disturbances (CFEDs), such as artificially transmitted very-low-frequency (VLF) radio waves, power line harmonics, and interference from the satellite platform itself, appear as horizontal lines. To exploit this feature, we proposed an algorithm based on computer vision technology that automatically recognizes these lines on the spe

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Fujita, Yoshio. "Identification of the (+2) Sequence of the CN Red System (A2Π-X2Σ) in Carbon Stars". International Astronomical Union Colloquium 106 (1989): 52. http://dx.doi.org/10.1017/s0252921100109959.

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Identification of spectral lines from λ7700 to λ8800 has been carried out in twenty-four carbon stars: AQ And, UU Aur, W CMa, Y CVn, X Cnc, ST Cas, WZ Cas, AX Cyg, RS Cyg, U Cyg, V 460 Cyg, RY Dra, UX Dra, BL Ori, W Ori, RX Peg, Z Psc, 19 Psc, S Set, Y Tau. VY UMa, HD 137613, HD 156074, and HD 182040. The spectrograms used for this purpose were obtained at the coude foci of spectrographs attached to 100-inch reflector of Mt. Wilson, 200-inch reflector of Mt. Palomar, and 74-inch reflector of Okayama. From the microphotometric tracings of each spectrogram, it was found that the main contributor

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Roth, M., A. Herrero, R. H. Mendez, R. P. Kudritzki, K. Butler, and H. G. Groth. "The Metal-Line Spectra of Central Stars of Planetary Nebulae." Symposium - International Astronomical Union 131 (1989): 317. http://dx.doi.org/10.1017/s0074180900138653.

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We present spectral descriptions based on high-resolution spectrograms of central stars of planetary nebulae, obtained with the ESO 3,6-m telescope + CASPEC (Cassegrain Echelle Spectrograph). We make preliminary determinations of stellar photospheric metal abundances, using non-LTE model atmospheres and non-LTE line formation calculations.

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Nanni, Loris, Sheryl Brahnam, Alessandra Lumini, and Gianluca Maguolo. "Animal Sound Classification Using Dissimilarity Spaces." Applied Sciences 10, no. 23 (2020): 8578. http://dx.doi.org/10.3390/app10238578.

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The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space t

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Han, Ying, Yalan Li, Jing Yuan, et al. "Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering." Atmosphere 14, no. 8 (2023): 1296. http://dx.doi.org/10.3390/atmos14081296.

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Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-E

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Griffin, R. F., and R. E. M. Griffin. "Mount Wilson Spectra of Standard Stars." Symposium - International Astronomical Union 111 (1985): 441–42. http://dx.doi.org/10.1017/s0074180900079171.

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We show part of a tracing of π Ceti, an example of a uniform series of high-quality tracings of standard stars covering the wavelength region 3850–4650 Å and derived from 10 Å/mm photographic spectrograms taken with the coudé spectrograph of the Mount Wilson 100-inch reflector.

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Chen, Rujia, Akbar Ghobakhlou, and Ajit Narayanan. "Hierarchical Residual Attention Network for Musical Instrument Recognition Using Scaled Multi-Spectrogram." Applied Sciences 14, no. 23 (2024): 10837. http://dx.doi.org/10.3390/app142310837.

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Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. Here, we propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (Chroma, Spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances the focus on relevant spectrogram parts through attention m

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Alia Hussein, Ahmed Talib Abdulameer, Ali Abdulkarim, Husniza Husni, and Dalia Al-Ubaidi. "Classification of Dyslexia Among School Students Using Deep Learning." Journal of Techniques 6, no. 1 (2024): 85–92. http://dx.doi.org/10.51173/jt.v6i1.1893.

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Dyslexia is a common learning disorder that affects children’s reading and writing skills. Early identification of Dyslexia is essential for providing appropriate interventions and support to affected children. Traditional methods of diagnosing Dyslexia often rely on subjective assessments and the expertise of specialists, leading to delays and potential inaccuracies in diagnosis. This study proposes a novel approach for diagnosing dyslexic children using spectrogram analysis and convolutional neural networks (CNNs). Spectrograms are visual representations of audio signals that provide detaile

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Jiang, Hao, Jianqing Jiang, and Guoshao Su. "Rock Crack Types Identification by Machine Learning on the Sound Signal." Applied Sciences 13, no. 13 (2023): 7654. http://dx.doi.org/10.3390/app13137654.

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Sound signals generated during rock failure contain useful information about crack development. A sound-signal-based identification method for crack types is proposed. In this method, the sound signals of tensile cracks, using the Brazilian splitting test, and those of shear cracks, using the direct shear test, are collected to establish the training samples. The spectrogram is used to characterize the sound signal and is taken as the input. To solve the small sample problem, since only a small amount of sound signal spectrogram can be obtained in our experimental test, pre-trained ResNet-18 i

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Han, Shuai, Heng Li, Mingchao Li, and Timothy Rose. "A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound." Applied Sciences 9, no. 17 (2019): 3484. http://dx.doi.org/10.3390/app9173484.

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Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classif

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Costantini, Giovanni, Valerio Cesarini, and Emanuele Brenna. "High-Level CNN and Machine Learning Methods for Speaker Recognition." Sensors 23, no. 7 (2023): 3461. http://dx.doi.org/10.3390/s23073461.

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Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or “traditional” Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. A custom CNN is compared to several pre-trained nets using image inputs of spectrograms and Cepstral-temporal (MFCC) graphs. AML approach based on acoustic feature extraction, selection and multi-class classification by means of a Naïve Bayes model is also cons

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Srilakshmi, Garaga, Vadakattu Sai Harsha, Kurakula Nitin, Bera Vamsi Krishna, and Osipilli David Raju. "Dysarthria Detection and Speech-to-Text Transcription Using Deep Learning and Audio Processing." Journal of Neonatal Surgery 14, no. 6S (2025): 567–73. https://doi.org/10.52783/jns.v14.2276.

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Dysarthria is a motor speech disorder affecting articulation, pitch, and rhythm due to neurological damage in the human body. Early detection is crucial for effective therapy. This study presents a novel dysarthria detection approach using Mel Frequency Logarithmic Spectrograms (MFLS) and Deep Convolutional Neural Networks (DCNN). Speech signals are preprocessed to extract MFLS, capturing essential frequency and temporal features. These spectrograms serve as input to a DCNN, which identifies patterns associated with dysarthric speech. The model was trained on publicly available datasets, achie

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Smietanka, Lukasz, and Tomasz Maka. "Enhancing Embedded Space with Low–Level Features for Speech Emotion Recognition." Applied Sciences 15, no. 5 (2025): 2598. https://doi.org/10.3390/app15052598.

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This work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances. In the experiments, we used two time-frequency representations (Mel and CQT spectrograms) and EmoDB and RAVDESS databases. As the results show, the proposed system improved the classification accuracy of both representations: 1.29% for CQT and 3.75% for Mel spectrogram compared to the typical CNN architecture for the EmoDB dataset and 3.02% for CQT and 0.63% for Mel spectrogram in the case o

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Abreu, Luís Daniel, Karlheinz Gröchenig, and José Luis Romero. "On accumulated spectrograms." Transactions of the American Mathematical Society 368, no. 5 (2015): 3629–49. http://dx.doi.org/10.1090/tran/6517.

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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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Ender Ozturk, Fatih Erden, and Ismail Guvenc. "RF-based low-SNR classification of UAVs using convolutional neural networks." ITU Journal on Future and Evolving Technologies 2, no. 5 (2021): 39–52. http://dx.doi.org/10.52953/qjgh3217.

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Unmanned Aerial Vehicles (UAVs), or drones, which can be considered as a coverage extender for Internet of Everything (IoE), have drawn high attention recently. The proliferation of drones will raise privacy and security concerns in public. This paper investigates the problem of classification of drones from Radio Frequency (RF) fingerprints at the low Signal-to-Noise Ratio (SNR) regime. We use Convolutional Neural Networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN

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Heap, S. R., D. J. Lindler та T. M. Lanz. "STIS Coronagraphic Observations of β Pictoris". Symposium - International Astronomical Union 202 (2004): 338–40. http://dx.doi.org/10.1017/s0074180900218184.

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We present recent coronagraphic observations of β Pictoris obtained with the Space Telescope Imaging Spectrograph (STIS) aboard the Hubble Space Telescope. The superb, high-resolution images show that the inner part of the disk is inclined by about 5° with respect to the main disk. Long-slit coronagraphic spectrograms oriented along the inner disk indicate that the reflectance of the inner disk is neutral over the spectral region, 3000-5600 Å.

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van de Wouwer, G., P. Scheunders, D. van Dyck, M. de Bodt, F. Wuyts, and P. H. van de Heyning. "Voice Recognition from Spectrograms: A Wavelet Based Approach." Fractals 05, supp01 (1997): 165–72. http://dx.doi.org/10.1142/s0218348x97000735.

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The performance of a pattern recognition technique is usually determined by the ability of extracting useful features from the available data so as to effectively characterize and discriminate between patterns. We describe a novel method for feature extraction from speech signals. For this purpose, we generate spectrograms, which are time-frequency representations of the original signal. We show that, by considering this spectrogram as a textured image, a wavelet transform can be applied to generate useful features for recognizing the speech signal. This method is used for the classification o

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Godbole, Shubham, Vaishnavi Jadhav, and Gajanan Birajdar. "Indian Language Identification using Deep Learning." ITM Web of Conferences 32 (2020): 01010. http://dx.doi.org/10.1051/itmconf/20203201010.

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Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated

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Samad, Salina Abdul, and Aqilah Baseri Huddin. "Improving spectrogram correlation filters with time-frequency reassignment for bio-acoustic signal classification." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 59. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp59-64.

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<p>Spectrogram features have been used to automatically classify animals based on their vocalization. Usually, features are extracted and used as inputs to classifiers to distinguish between species. In this paper, a classifier based on Correlation Filters (CFs) is employed where the input features are the spectrogram image themselves. Spectrogram parameters are carefully selected based on the target dataset in order to obtain clear distinguishing images termed as call-prints. An even better representation of the call-prints is obtained using spectrogram Time-Frequency (TF) reassignment.

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