scholarly journals Text–Independent Speaker Recognition Using Two–Dimensional Information Entropy

2015 ◽  
Vol 66 (3) ◽  
pp. 169-173 ◽  
Author(s):  
Boško Božilović ◽  
Branislav M. Todorović ◽  
Miroslav Obradović
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Information Entropy ◽  
Speaker Recognition ◽  
Speech Signal ◽  
Experimental Results ◽  
Two Dimensional ◽  
Real Numbers ◽  
The Past ◽  
Signal Features

AbstractSpeaker recognition is the process of automatically recognizing who is speaking on the basis of speaker specific characteristics included in the speech signal. These speaker specific characteristics are called features. Over the past decades, extensive research has been carried out on various possible speech signal features obtained from signal in time or frequency domain. The objective of this paper is to introduce two-dimensional information entropy as a new text-independent speaker recognition feature. Computations are performed in time domain with real numbers exclusively. Experimental results show that the two-dimensional information entropy is a speaker specific characteristic, useful for speaker recognition.

Amplification of Signal Features Using Variance Fractal Dimension Trajectory

2012 ◽  
pp. 263-277
Author(s):  
Witold Kinsner ◽  
Warren Grieder
Keyword(s):  
Fractal Dimension ◽  
Time Domain ◽  
Speech Signal ◽  
Experimental Results ◽  
Unit Distance ◽  
Detection Techniques ◽  
Signal Features ◽  
The Individual ◽  
Selection Of

This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory (VFDT) algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. Observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.

scholarly journals Evaluation of speech signal features extraction methods

2020 ◽  
Vol 2 (1) ◽  
pp. 69-78
Author(s):  
Ismail Shayeb ◽  
Naseem Asad ◽  
Ziad Alqadi ◽  
Qazem Jaber
Keyword(s):  
Speech Signal ◽  
High Speed ◽  
Research Paper ◽  
Extraction Methods ◽  
Features Extraction ◽  
Experimental Results ◽  
Extraction Time ◽  
Digital Signals ◽  
Human Speech ◽  
Signal Features

Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

scholarly journals IMAGE FAST DEHAZING ALGORITHM BASED ON DARK CHANNEL PRIOR IMPROVEMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 585-589
Author(s):  
J. W. Li ◽  
L. Liu ◽  
J. W. Jiang ◽  
Y. Hu ◽  
X. Q. Han ◽  
...  
Keyword(s):  
Gamma Function ◽  
Information Entropy ◽  
Experimental Results ◽  
Guided Filter ◽  
Dark Channel Prior ◽  
Two Dimensional ◽  
Image Dehazing ◽  
Running Time ◽  
Dark Channel ◽  
Intensity Image

Abstract. Aiming at the long-running time and the defogging image darkening problem in the dark channel prior algorithm, a fast deaeration algorithm based on the guided filter and improved two-dimensional gamma function for dark channel prior image is proposed. The algorithm uses the guided filter instead of the soft matting to obtain the image transmittance. The summation operation in the window replaces the quadrature operation in the window to reduce the complexity of the algorithm, and the image is processed by the two-dimensional gamma function. The brightness is adjusted to increase the brightness of the dark areas of the image, improve the contrast of the image, and enhance the image's performance in detail. The experimental results show that compared with the dark channel prior defogging algorithm and other image dehazing algorithms, the image fast dehazing algorithm based on dark channel prior improvement has high effective detail intensity, image information entropy and average gradient. The running time of the dark channel prior defogging algorithm is reduced, which effectively solves the long running time and the defogging image darkness problem of the dark channel prior defogging algorithm and has good robustness, and improves the quality and display effects of defogging image.

scholarly journals An Adaptive Spectral Kurtosis Method Based on Optimal Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yanli Yang ◽  
Ting Yu
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Experimental Results ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
The Core ◽  
Factors Influencing ◽  
The Time Domain

As a useful tool to detect protrusion buried in signals, kurtosis has a wide application in engineering, for example, in bearing fault diagnosis. Spectral kurtosis (SK) can further indicate the presence of a series of transients and their locations in the frequency domain. The factors influencing kurtosis values are first analyzed, leading to the conclusion that amplitude, not the frequency of signals, and noise make major contribution to kurtosis values. It is helpful to detect impulsive components if the components with big amplitude are removed from composite signals. Based on this cognition, an adaptive SK algorithm is proposed in this paper. The core steps of the proposed SK algorithm are to find maxima, add window around maxima, merge windows in the frequency domain, and then filter signals according to the merged window in the time domain. The parameters of the proposed SK algorithm are varying adaptively with signals. Some experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

Amplification of Signal Features Using Variance Fractal Dimension Trajectory

2010 ◽  
Vol 4 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Witold Kinsner ◽  
Warren Grieder
Keyword(s):  
Fractal Dimension ◽  
Time Domain ◽  
Speech Signal ◽  
Ambient Noise ◽  
Unit Distance ◽  
Detection Techniques ◽  
Signal Features ◽  
The Individual ◽  
Selection Of

This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory (VFDT) algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. Observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.

scholarly journals High Resolution Imaging from Azimuth Missing SAR Raw Data via Segmented Recovery

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 336 ◽  
Author(s):  
Yulei Qian ◽  
Daiyin Zhu
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Greedy Algorithms ◽  
Two Dimensional ◽  
Raw Data ◽  
Reference Function ◽  
Resolution Imaging ◽  
Frequency Aliasing ◽  
Range Migration ◽  
Imaging Algorithms

Synthetic Aperture Radar (SAR) raw data missing occurs when radar is interrupted by various influences. In order to cope with this problem, a new method is proposed to focus the azimuth missing SAR raw data via segmented recovery in this paper. A reference function in time domain is designed to make the missing raw data sparser in two dimensional frequency domain. Afterwards, greedy algorithms are available to recover the missing data in two dimensional frequency domain. In addition, in order to avoid range frequency aliasing problem caused by reference function multiplication in time domain, the missing raw data is split into several parts in range direction and is recovered with a segmented recovery strategy. Then, the recovered raw data is available to be focused with traditional SAR imaging algorithms. The range migration algorithm is chosen to deal with the recovered raw data in this paper. Point target and area target simulations are carried out to validate the effectiveness of the proposed method on azimuth missing SAR raw data. Moreover, the proposed method is implemented on real SAR data in order to further provide convincing demonstration.

Dimension Reduction Analysis of Signal Manifold for Vowels in Time and Frequency Domain

2021 ◽  
Vol 17 ◽  
pp. 69-74
Keyword(s):  
Dimension Reduction ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Two Dimensional ◽  
Time Domain Simulation ◽  
Time Frequency ◽  
Reduction Methods

In this paper, the LLE and ISOMAP algorithms in manifold learning are applied them to the analysis of vowel signals in time and frequency domain. Time domain simulation results show that the two dimensionality reduction methods can implement two-dimensional visualization of signals while preserving the high-dimensional manifold structure of original signals as much as possible. The time-frequency domain dimension reduction analysis of vowel signal manifold effectively solves the problem that high-dimensional speech signals can’t be intuitively felt, and provides a new potential way for signal classification. The frequency domain analysis is further optimized on the basis of time domain simulation. Because half of the amplitude values in DFT is used in the simulation, the two-dimensional manifold of the signal is roughly linearly distributed, which can effectively reduce redundancy and make the signal more compactly expressed in the frequency domain

Construction of a two-dimensional-VWOOC based on time domain/frequency domain optical CDMA system

2020 ◽  
Vol 16 (5) ◽  
pp. 384-389
Author(s):  
Zhuo Lu ◽  
Ye Lu ◽  
Chuan-qi Li ◽  
Peng Zhou
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Optical Cdma ◽  
Cdma System ◽  
Two Dimensional
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