scholarly journals An approach to spectral analysis of psychologically influenced speech

2017 ◽  
Vol 7 (1.2) ◽  
pp. 66
Author(s):  
Bhagyalaxmi Jena ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Information Content ◽  
Power Spectral Density ◽  
Speech Signal ◽  
Power Spectral ◽  
Normal Speech

The significant part of any speech signal lies in the information content and the emotions contents like stress or fatigue at a particular period of time. The classification of various types of stress and their effects are defined here. To analyze the changes in stressed speech than that of the normal speech, a database has been created which has investigated the stress among students during the examination in our college. In this paper, the spectral analysis of speech is done where emphasis has been given in the parameters like Fast Fourier Transform (FFT), spectrogram and Power Spectral Density (PSD). These parameters have been simulated using MATLAB codes. The comparison of the mentioned parameters is also done between a normal speech and a psychological stressed speech.

scholarly journals Pewilayahan Siklus Suhu Permukaan Laut di Perairan Indonesia Menggunakan Metode Power Spectral Density (PSD)

PRISMA FISIKA ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 85
Author(s):  
Syarifah Resha Fadziella ◽  
Yoga Satria Putra ◽  
Arie Antasari Kushadiwijayanto
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Power Spectral Density ◽  
Power Spectral

Penelitian tentang siklus suhu permukaan laut (SPL) dominan terbesar pertama dan kedua di Perairan Indonesia telah dilakukan menggunakan metode Power Spectral Density (PSD) berdasarkan data time series SPL selama 40 tahun (1979-2018). Dari analisis yang dilakukan siklus dominan terbesar pertama adalah siklus 12.15 bulan (annual) dan siklus 6 bulan (semiannual). Siklus 12.15 bulan (annual) cenderung berada di perairan Utara dan perairan Selatan Indonesia sedangkan siklus 6 bulan (semiannual) cenderung berada di kawasan ekuator kecuali perairan Ekuatorial Samudra Hindia. Kemudian, siklus dominan terbesar kedua memiliki beragam periode seperti siklus setengah tahun (semiannual), tahunan (annual) dan siklus antar tahunan (interannual). Siklus setengah tahun (semiannual) berada di perairan Utara dan perairan Selatan Indonesia, siklus tahunan (annual) berada di kawasan ekuator, dan siklus antar tahunan (interannual) berada di perairan Barat Sumatera, Selat Makassar, Teluk Tomini, Laut Halmahera, dan di perairan Papua.Kata Kunci : Suhu permukaan laut, Perairan Indonesia, Power Spectral Density (PSD), dan Fast Fourier Transform (FFT).

scholarly journals The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Di ◽  
Xingwei An ◽  
Wenxiao Zhong ◽  
Shuang Liu ◽  
Dong Ming
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Individual Identification ◽  
Frequency Range ◽  
Cross Spectrum ◽  
Power Spectral ◽  
Phase Lags ◽  
Individual Features

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

scholarly journals Power spectral analysis of heart rate in hypothyroidism

2000 ◽  
pp. 327-333 ◽  
Author(s):  
V Cacciatori ◽  
ML Gemma ◽  
F Bellavere ◽  
R Castello ◽  
ME De Gregori ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Cardiovascular System ◽  
Power Spectral Density ◽  
Power Spectral Analysis ◽  
Control Subjects ◽  
Power Spectral ◽  
Hypothyroid Patients

OBJECTIVE: The aim of the present study was to evaluate the impact of hypothyroidism on the autonomic regulation of the cardiovascular system by analysing separately sympathetic and parasympathetic influences on the heart. DESIGN: In seven newly diagnosed untreated hypothyroid patients we analysed power spectral density of heart rate cyclic variations at rest, while lying, and while standing. The same protocol was repeated after the induction of stable euthyroidism by levothyroxine (L-T(4)) treatment. The results were also compared with those obtained from seven age-, sex- and body mass index-matched control subjects. METHODS: Heart rate variability was evaluated by autoregressive power spectral analysis (PSA). This method allows reliable quantification of low frequency (LF) and high frequency (HF) components of the heart rate power spectral density. These are considered to be under mainly sympathetic and purely parasympathetic control respectively. In addition, heart rate variations during deep breathing, lying to standing, and Valsalva's manoeuvre were assessed. RESULTS: PSA showed a sharp reduction in the HF (parasympathetic) component in hypothyroid subjects compared with controls (lying, 29.4+/-5.4 vs 47.7+/-6.3 normalized units (NU) (means +/- s.e.m.), P<0.05; standing, 14.0+/-3.5 vs 32.1+/-3.6NU, P<0.005). Conversely, the LF (mainly sympathetic) component was higher in hypothyroid subjects than in controls (lying, 61.6+/-6.4 vs 45.4+/-6.7 NU; standing, 71.7+/-8.0 vs 53.1+/-5.6NU), this difference being significant in the standing position. Hence, the LF/HF ratio, which is considered an index of sympathovagal balance, was increased in hypothyroid subjects while both lying (2.75+/-0.6 vs 1.16+/-0.3; P<0.05) and standing (10.0+/-3.7 vs 1.85+/-0.3; P<0. 02). Total heart rate variability, expressed as total power spectral density, was lower in hypothyroid patients than in control subjects, this difference being significant in the lying position (574+/-126 vs 2302+/-994ms(2), P<0.05). In patients re-examined after L-T(4) therapy, complete normalization of cardiovascular parameters was observed (LF/HF ratio, lying, 1.26+/-0.4; standing, 2.56+/-0.8, both P<0.01 vs baseline values). The response to conventional cardiovascular autonomic tests was not significantly different between hypothyroid patients and healthy controls, and did not change in patients after therapy. CONCLUSIONS: These results suggest that, contrary to the clinical picture, thyroid hormone deficiency is associated with an increased sympathetic influence on the autonomic cardiovascular system. The changes in sympathetic function could be explained by a secondary adaptation to an altered cardiovascular responsiveness.

Resolving the Issues of Capon and APES Approach for Projecting Enhanced Spectral Estimation

2016 ◽  
Vol 6 (2) ◽  
pp. 725
Author(s):  
Kantipudi MVV Prasad ◽  
H.N. Suresh
Keyword(s):  
Signal Processing ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Simple Technique ◽  
Spectral Estimation ◽  
Research Work ◽  
Research Community ◽  
The Past ◽  
Power Spectral

There are various applications on signal processing that is highly dependent on preciseness and accuracy of the outcomes in spectrum of signals. Hence, from the past two decades the research community has recognized the benefits, significance, as well as associated problems in carrying out a model for spectral estimation. While in-depth investigation of the existing literatures shows that there are various attempts by the researchers to solve the issues associated with spectral estimations, where majority of teh research work is inclined towards addressing problems associated with Capon and APES techniques of spectral analysis. Therefore, this paper introduces a very simple technique towards resolving the issues of Capon and APES techniques. The outcome of the study was analyzed using correlational factor and power spectral density to find the proposed system offers better spectral estimations compared to existing system.

scholarly journals Optimum Resolution Bandwidth for Spectral Analysis of Stationary Random Vibration Data

1993 ◽  
Vol 1 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Allan G. Piersol
Keyword(s):  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Random Vibration ◽  
Damping Ratio ◽  
Frequency Resolution ◽  
Power Spectral ◽  
Averaging Time ◽  
Resolution Bandwidth

This article presents a methodology for selecting the frequency resolution bandwidth for the spectral analysis of stationary random vibration signals in an optimum manner. Specifically, the resolution bandwidth that will produce power spectral density estimates with a minimum mean square error is determined for any given measurement duration (averaging time), and methods of approximating the optimum bandwidth using practical spectral analysis procedures are detailed. The determination of the optimum resolution bandwidth requires an estimate for the damping ratio of the vibrating structure that produced the measured vibration signal and the analysis averaging time. It is shown that the optimum resolution bandwidth varies approximately with the 0.8 power of the damping ratio and the bandwidth center frequency, and the −0.2 power of the averaging time. Also, any resolution bandwidth within ±50% of the optimum bandwidth will produce power spectral density (PSD) estimates with an error that is no more than 25% above the minimum achievable error. If a damping ratio of about 5% for structural resonances is assumed, a constant percentage resolution bandwidth of 1/12 octave, but no less than 2.5 Hz, will provide a near optimum PSD analysis for an averaging time of 2 seconds over the frequency range from 20 to 2000 Hz. A simple scaling formula allows the determination of appropriate bandwidths for other damping ratios and averaging times.

scholarly journals Staggered-PRT Sequences for Doppler Weather Radars. Part I: Spectral Analysis Using the Autocorrelation Spectral Density

2017 ◽  
Vol 34 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Sebastián M. Torres ◽  
David A. Warde
Keyword(s):  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Weather Radar ◽  
Real Data ◽  
Pulse Repetition ◽  
Spectral Processing ◽  
Weather Radars ◽  
Power Spectral ◽  
Repetition Time

AbstractThe autocorrelation spectral density (ASD) was introduced as a generalization of the classical periodogram-based power spectral density (PSD) and as an alternative tool for spectral analysis of uniformly sampled weather radar signals. In this paper, the ASD is applied to staggered pulse repetition time (PRT) sequences and is related to both the PSD and the ASD of the underlying uniform-PRT sequence. An unbiased autocorrelation estimator based on the ASD is introduced for use with staggered-PRT sequences when spectral processing is required. Finally, the strengths and limitations of the ASD for spectral analysis of staggered-PRT sequences are illustrated using simulated and real data.

Sign in / Sign up

Export Citation Format

Share Document