Spectrogram Analysis Research Articles

Clinical Usefulness of Korean Items for the Differential Diagnosis of Adductor Spasmodic Dysphonia

Background and Objectives Adductor spasmodic dysphonia (ADSD) is a disease in which symptoms such as voice break, strained voice, and voice tremor occur during vocalization due to abnormal spasms of the adductor muscles of the vocal cords. In this study, we aimed to confirm the clinical effectiveness of recently developed Korean items for the differential diagnosis of ADSD and muscle tension dysphonia (MTD).Materials and Method Twenty patients who were diagnosed with ADSD and improved with treatment were set as the ADSD group, 7 patients diagnosed with MTD and received voice therapy as the MTD group, and 7 normal patients without voice disorders were set as the control group, and the medical records and voice test results were compared and analyzed. Recently developed Korean questions were read by the patient, and the degree of voice tremor, strained voice was evaluated. In addition, the recorded voice was analyzed with a spectrogram.Results A statistically significant difference between the two patient groups was confirmed in the auditory perceptual evaluation of voiced sentences and phrases, tense voiceless sentences and phrases of Korean items. Spectrogram analysis revealed that voice breaks were more frequent in the ADSD group during voiced sentences, and wide-spaced vertical striations were more prominent in the ADSD group during voiced sentences, phrases, and tense voiceless phrases.Conclusion It was confirmed that Korean items for the differential diagnosis of ADSD are useful in identifying speech task specificities in ADSD patients through spectrogram analysis as well as auditory perceptual evaluation.

ADAPTIVE FILTERING AND MACHINE LEARNING METHODS IN NOISE SUPPRESSION SYSTEMS, IMPLEMENTED ON THE SoC

Context. Modern video conferencing systems work in different noise environments, so preservation of speech clarity and provision of quick adaptation to changes in this environment are relevant tasks. During the development of embedded systems, finding a balance between resource consumption, performance, and signal quality obtained after noise suppression is necessary. Systems on a chip allow us to use the power of both processor cores available on the hardware platform and FPGAs to perform complex calculations, which contributes to increasing the speed or reducing the load on the central SoC cores. Objective. To conduct a comparative analysis of the noise suppression quality in audio signals by an adaptive filtering algorithm and a filtering algorithm using machine learning based on the RNNoise neural network in noise suppression devices on the technological platform SoC. Method. Evaluation using objective metrics and spectrogram analysis using the Librosa library in Python. Neural network training and model design are performed on the basis of Python and Torch tools. The Vitis IDE package was used for the neural network implementation on the platform SoC. Results. The analysis of two noise suppression methods using the adaptive Wiener filter and the RNNoise neural network was performed. In the considered scenarios, it was determined that the neural network shows better noise suppression results according to the analysis of spectrograms and objective metrics. Conclusions. A comparative analysis of the effectiveness of noise suppression algorithms based on adaptive filters and a neural network was performed for scenarios with different noise environments. The results of objective SIGMOS metrics were obtained to evaluate the quality of the received audio signal. In addition, the possibility of running the RNNoise neural network on the technological platform SoC ZYNQ 7000 was verified.

Proteinoid-polyaniline neuromorphic composites for audio recognition

Abstract We present an innovative neuromorphic system using a proteinoid-polyaniline (PANI) composite for recognition of audio inputs of the English alphabet. Neuromorphic devices, which draw inspiration from the neural networks of the brain, have emerged as very promising potential solutions for efficient signal processing. The proteinoid-PANI composite was synthesized through a template-free method, resulting in a unique nanostructure consisting of both nanorods and nanospheres. Principal component analysis (PCA), spectrogram analysis, and temporal spiking response analysis were among the signal processing methods used to examine the composite's audio response to English alphabet stimuli. The system showed a moderate positive correlation between input and output signals, unique time-frequency response patterns, and convoluted spiking behaviour. In addition, the output amplitude showed less variation compared to the input, while maintaining the same temporal characteristics.
Microscopic analysis provided detailed information about the morphology of the composite. The nanorods displayed an optimal aspect ratio and had diameters of around 
100~nm, while the nanospheres varied in size, ranging from 200 to 500~nm in diameter. The nanostructure, morphological characteristics, and signal processing properties of the proteinoid-PANI composite demonstrate its potential for advanced applications in neuromorphic computing and signal processing, particularly in speech recognition and human-machine interaction.

Evaluation of the Effect of Functional Contouring of the Palatal Vault of Maxillary Complete Denture on Clarity of Speech Sounds in Edentulous Patients: An In Vivo Study

ABSTRACT Background: The articulatory system, which consists of the tongue, lips, teeth, hard palate, and soft palate, is a component of phonetics, the study of speech sounds. Speech sounds are negatively impacted by the lack of teeth and surrounding tissues in edentulous people. Phonetics has not gotten the same level of attention as denture mechanics and aesthetics, despite notable developments in these areas. The purpose of this research is to increase speech clarity in edentulous individuals by contouring the palatal vault of maxillary full dentures. Materials and Methods: Sixty edentulous individuals, who spoke Tamil or English well and were between the ages of 45 and 75, were chosen. Speech sounds from each individual were captured both before and after the maxillary full dentures’ palatal vault was functionally contoured using visible light cure acrylic resin. For a quantitative evaluation, spectrograms were used to examine the speech samples. Results: For most patients, functional contouring of the palatal vault markedly enhanced speech sound quality. The spectrogram analysis’s arbitrary results showed that most participants’ voice clarity had improved by 30% and that the time it took patients to attain clear speech after having a denture had been significantly reduced. Conclusion: The research comes to the conclusion that edentulous patients’ speech clarity is improved by functionally contouring the palatal vault using visible light cure acrylic resin in maxillary full dentures. By using this technique, patients are more satisfied with their dentures and the adaption time is shortened.

Retraction Note: Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique

Retraction Note: Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique

Comparative Analysis of Spectrogram and MFCC Representations for Speech Emotion Recognition Using Machine Learning

Comparative Analysis of Spectrogram and MFCC Representations for Speech Emotion Recognition Using Machine Learning

Vibration spectrogram analysis for bearing fault diagnosis based on grad-cam for feature selection and statistical approach

Vibration spectrogram analysis for bearing fault diagnosis based on grad-cam for feature selection and statistical approach

Unsteadiness in vacuum ejector and their sources

This study investigates unsteadiness in the secondary chambers of a vacuum ejector during its transient startup phase. Experiments reveal unsteadiness at various frequencies across different nozzle pressure ratios. These frequencies are categorized as symmetric (in-phase) or asymmetric (counter-phase) through cross-spectral phase analysis. Dynamic mode decomposition, using time-resolved schlieren images, elucidates the corresponding flow structures and acoustic fields. The mode shapes display alternating density gradients along both the longitudinal and lateral axes, which explain the oscillatory behavior of the jet. These oscillations arise from instabilities in the jet's shear layer and acoustic disturbances due to the jet being confined within a duct. Using empirical relations, spectrogram analysis, time-resolved schlieren imaging, and classical wave theory, the sources of unsteadiness in the secondary chamber are identified as jet screech, its harmonics, and modes due to duct acoustics (symmetric and asymmetric). Additionally, the screech feedback loop in the vacuum ejector is found to be distinct from that in open jets, and the effect of screech on duct-mode acoustics is also observed.

Identifying chaotic dynamics in noisy time series through multimodal deep neural networks

Abstract Chaos detection is the problem of identifying whether a series of measurements is being sampled from an underlying set of chaotic dynamics. The unavoidable presence of measurement noise significantly affects the performance of chaos detectors, as discerning chaotic dynamics from stochastic signals becomes more challenging. This paper presents a computationally efficient multi-modal deep neural network tailored for chaos detection by combining information coming from the analysis of time series, recurrence plots and spectrograms. The proposed approach is the first one suitable for multi-class classification of chaotic systems while being robust with respect to measurement noise, and is validated on a dataset of 15 different chaotic and non-chaotic dynamics subject to white, pink or brown coloured noise.

2D Spectrogram analysis using vision transformer to detect mispronounced Arabic utterances for children

Pronunciation feedback is essential for teaching languages to children, urging the need to create computer-assisted pronunciation training (CAPT) systems to automate this process. Most of the current CAPT systems for Arabic Language examined a wide age range, including children, with a dominant adults’ portion. However, these systems did not consider many challenges facing children’s voice, in terms of the acoustic difference between children and adults. Moreover, due to the lack of publicly Arabic datasets, Arabic-related systems were evaluated on datasets consisting of the sound of Arabic letters, with minor efforts to contain words or sentences. In this paper, we propose the Arabic Utterance Mispronunciation Detector for Children (AUMD-Child) system that detects the mispronunciation of children for Arabic words at real-time. The proposed system is based on the fusion of Vision Transformer (ViT) and transfer learning-based model (AlexNet). A novel public dataset is constructed, in which 16 words have been collected from children aged between 7 and 12 years old to evaluate our proposed system. The experimental results, compared to the state-of-the-art models in literature, showed that the accuracy of our proposed AUMD-Child system exceeded the handcrafted features-based models by an average of 7 %, the CNN features-based models using AlexNet and SVM by 5 %, transfer learning-based model using AlexNet by 4 %, and transformers-based model using ViT by 2 %, achieving an average accuracy of 91.81 % for detecting the mispronunciation patterns in Arabic words but in an average processing time of 33 ms, exceeding the other state-of-the-art models with 13 %, 0.03 %, 0.06 %, and 23 % respectively.

Analysis of Student Learning Behavior using Process Mining and Spectrogram

This study aims to present an analysis using Spectrogram Analysis, Correlation Analysis, and Multiple Linear Regression Analysis to examine factors affecting the efficiency of teaching management and the relationship between the frequency of attendance and the duration of attendance on academic achievement. The tools employed in this study include event log data analyzed using Process Mining techniques with the Fuzzy Miner algorithm and Spectrogram Analysis through the Matplotlib Library of Python. The sample group consisted of 247 undergraduate students from a private university in Thailand, selected using a purposive sampling method. The results of the Spectrogram Analysis reveal a clear distinction in the continuity of the learning process between groups with academic achievement above 70% and below 70%. The frequency of attendance is positively correlated with the duration of attendance at a statistical significance level of 0.01, and the frequency of attendance (Beta = 0.537) is a significant factor affecting the efficiency of teaching management at a statistical significance level of 0.01. Therefore, it is possible to integrate techniques of Process Mining, Spectrogram Analysis, Correlation Analysis, and Multiple Linear Regression Analysis to discover and confirm methods for developing teaching processes, improving teaching quality, enhancing students' learning experiences, and driving e-learning systems to achieve better academic outcomes, promoting continuous awareness and learning.

Effect of Aging on Long-Term Functional Outcomes After Open Partial Laryngectomy.

Open partial horizontal laryngectomies (OPHLs) represent a comparable alternative to total laryngectomy and nonsurgical protocols in selected cases. While short-term functional outcomes of OPHLs have been widely investigated, few have focused on the effect of aging on residual laryngeal structures. Retrospective cohort study. Tertiary care academic center. Patients who underwent OPHLs after at least 1 year follow-up and optimal functional rehabilitation were included in the study. Swallowing function was assessed according to PAS (Penetration aspiration scale) and Pooling scores. Spectrogram analysis of voice was conducted according to Yanagihara classification and acoustic parameters were also recorded. Subjective questionnaire data about phonation and swallowing were also recorded. Data obtained were compared among patients according to age at time of surgery, evaluation and duration of follow-up. Ninety-seven patients were enrolled with a mean age at surgery and evaluation of 63 and 70 years old, respectively. Median follow-up length was 5 years. OPHL type II was mostly performed. No significant correlation was observed between most of the analyzed variables and patient's age at the time of surgery and at the time of evaluation. Some acoustic parameters were negatively correlated with follow-up length, while Jitter, NHR (Noise-Harmonic Ratio), and Global grade and Roughness were significantly higher in patients >65 years old. Patients who complete rehabilitation reach equally good results as their younger peers with stability over time. Finally, the effects of aging on residual larynx are of minor entity compared to the nonoperated patients. Level IV-retrospective cohort study.

Spectrogram Analysis for Lovebird Voice Characterization Based on Age

Lovebirds are known to be energetic and active birds. The chirps produced by these birds are also known to be very melodious. This study aims to analyze the sound characterization of lovebirds of various age ranges such as the amplitude and frequency produced. The method used in this research is a qualitative analysis non-experiment. This research uses spectrogram analysis in the process of processing existing data. The results obtained in this study are the frequency values at the age of 1 month, 2 months, 4 months, and 7 months in a row, namely 3257.81 Hz, 3416.01 Hz, 3533.20 Hz, and 3937.50 Hz. In addition, the amplitude values obtained at the age of 1 month, 2 months, 4 months, and 7 months were 2.96 Pct FS, 2.99 Pct FS, 3.29 Pct FS, and 8.45 Pct FS, respectively. This shows that the increase in frequency and amplitude values is proportional to the increase in age. Then it is also obtained that the frequency value produced in the age range of one to seven months is in the range of 3000 Hz - 4000 Hz.

가상악기를 활용한 거문고와 첼로의 음색비교 연구

This study aimed to compare and analyze the timbre of the geomungo and cello, focusing on harmonic analysis to examine the tonal characteristics of both instruments. Using pizzicato, a common playing technique for both instruments, A2, A3, and A4 notes were recorded, and the sound sources were generated using virtual instruments. By comparing the timbre of the two instruments through spectrum, spectrogram, envelope, and sound pressure analysis methods, it was found that the geomungo showed a stronger influence of harmonics than the fundamental frequency, characterized by a very fast attack and rapid release, which are key factors in forming the geomungo’s clear and bright tone. In the case of the cello, the fundamental frequency had a stronger influence than the harmonics, and it was found that the presence of subharmonics and non-harmonic partials, along with a fast attack and slow release, contribute to its deep and rich tone. This study provides an understanding of the tonal characteristics of the geomungo and cello, and it is hoped that this will contribute to the harmonious fusion of traditional Korean and Western music, as well as foster new musical diversity and creativity.

Detection and Classification of Obstructive Sleep Apnea Using Audio Spectrogram Analysis

Sleep disorders are steadily increasing in the population and can significantly affect daily life. Low-cost and noninvasive systems that can assist the diagnostic process will become increasingly widespread in the coming years. This work aims to investigate and compare the performance of machine learning-based classifiers for the identification of obstructive sleep apnea–hypopnea (OSAH) events, including apnea/non-apnea status classification, apnea–hypopnea index (AHI) prediction, and AHI severity classification. The dataset considered contains recordings from 192 patients. It is derived from a recently released dataset which contains, amongst others, audio signals recorded with an ambient microphone placed ∼1 m above the studied subjects and apnea/hypopnea accurate events annotations performed by specialized medical doctors. We employ mel spectrogram images extracted from the environmental audio signals as input of a machine-learning-based classifier for apnea/hypopnea events classification. The proposed approach involves a stacked model which utilizes a combination of a pretrained VGG-like audio classification (VGGish) network and a bidirectional long short-term memory (bi-LSTM) network. Performance analysis was conducted using a 5-fold cross-validation approach, leaving out patients used for training and validation of the models in the testing step. Comparative evaluations with recently presented methods from the literature demonstrate the advantages of the proposed approach. The proposed architecture can be considered a useful tool for supporting OSAHS diagnoses by means of low-cost devices such as smartphones.

Anterior thalamic nucleus local field potentials during focal temporal lobe epileptic seizures.

To analyze the local field potentials (LFPs) in patients with focal drug-resistant epilepsy (DRE) from the anterior nucleus of the thalamus (ANT) during inter-ictal state and seizure state. ANT stereotactic EEG (SEEG) recordings were studied in four patients with focal temporal lobe epilepsy. SEEG data was classified as inter-ictal and ictal state and sub-categorized into electrographic (ESz), focal aware seizure (FAS), focal with impaired awareness (FIA), or focal to bilateral tonic-clonic seizure (FBTC). LFP was analyzed at 4 Hz, 8 Hz, 16 Hz, 32 Hz, high gamma (100 Hz), and ripples (200 Hz) using spectrogram analysis and a statistical comparison of normalized power spectral density (PSD) averaged during seizures versus pre-ictal baseline segments. The LFP recordings were analyzed for 162 seizures (127 ESz, 23 FAS, 6 FIA, and 6 FBTC). Based on time-frequency data (spectrogram), a broad band of activity, occurring between 2 and 6 Hz and centered at 4 Hz, and thin-band activity occurring specifically at 8 Hz on the frequency spectrogram were observed during the inter-ictal state. Statistically significant changes in LFP-PSD were seen for FAS, FIA, and FBTC. We observed a significant gain in LFP at the lower frequency band during FAS at 4 Hz, FIA, and FBTC at 4, 8, and 16 Hz while also observing increases at higher frequencies during FBTC at 100 and 200 Hz and a decrease during FAS seizures at 32 Hz. In contrast, no significant change in LFP power was seen for electrographic seizures. Our observations from a limited dataset indicate that all clinical seizure types, but not electrographic seizures, caused a change in ANT-LFP based on the magnitude of the associated power spectral density (PSD). Future work will be needed to validate the use of ANT-LFP at these frequencies as accurate measurements of seizure occurrence and severity. This work represents a first step toward understanding ANT thalamic LFP patterns during focal seizures and developing adaptive DBS strategies.

Vision Transformer Based Classification of Neurological Disorders from Human Speech

In this study, we introduce a transformative approach to achieve high-accuracy classification of distinct health categories, including Parkinson's disease, Multiple Sclerosis (MS), healthy individuals, and other categories, utilizing a transformer-based neural network. The cornerstone of this approach lies in the innovative conversion of human speech into spectrograms, which are subsequently transformed into visual images. This transformation process enables our network to capture intricate vocal patterns and subtle nuances that are indicative of various health conditions. The experimental validation of our approach underscores its remarkable performance, achieving exceptional accuracy in differentiating Parkinson's disease, MS, healthy subjects, and other categories. This breakthrough opens doors to potential clinical applications, offering an innovative, non-invasive diagnostic tool that rests on the fusion of spectrogram analysis and transformer-based models.

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder

Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model.

Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings

In this paper, we review the main signal processing tools of Music Information Retrieval (MIR) from audio data, and we apply them to two recordings (by Leslie Howard and Thomas Rajna) of Franz Liszt’s Étude S.136 no.1, with the aim of uncovering the macro-formal structure and comparing the interpretative styles of the two performers. In particular, after a thorough spectrogram analysis, we perform a segmentation based on the degree of novelty, in the sense of spectral dissimilarity, calculated frame-by-frame via the cosine distance. We then compare the metrical, temporal and timbrical features of the two executions by MIR tools. Via this method, we are able to identify in a data-driven way the different moments of the piece according to their melodic and harmonic content, and to find out that Rajna’s execution is faster and less various, in terms of intensity and timbre, than Howard’s one. This enquiry represents a case study able to show the potentialities of MIR from audio data in supporting traditional music score analyses and in providing objective information for statistically founded musical execution analyses.

Distinguishing Hawai‘i Creole neva and néva: prosodic evidence from podcast interviews

Abstract This paper investigates prosodic and contextual differentiation strategies between preverbal past tense negator neva and adverb never in Hawai‘i Creole. It aims to demonstrate differing syntactic restrictions and advocates for treating these words as two distinct morphemes. The analyses are based on phonological data gathered from interviews uploaded onto YouTube by Hawaiiverse, a Local podcast. As demonstrated through spectrogram analyses, HC néva (=ɴᴇᴠᴇʀ) is marked by a stressed accent on its first syllable and prominence on word-initial /n/, whereas neva (=ᴅɪᴅɴ’ᴛ) is marked by a lack of these features under typical circumstances. This suggests that Hawai‘i Creole morphophonology depends more on stress-timed features than previously researched (cf. syllable-timed features). Neva-néva ambiguity may arise when stress does not clearly indicate which word is being used, and when context cannot be relied upon to distinguish meaning. By exploring these intricacies, this investigation offers insight into how future researchers may approach analysing other English-lexified creoles (and varieties of English) which also use never as a preverbal past tense negator.