CLASSIFICATION OF CLOSED- AND OPEN-SHELL PISTACHIO NUTS USING VOICE-RECOGNITION TECHNOLOGY

2004 ◽  
Vol 47 (2) ◽  
pp. 659-664 ◽  
Author(s):  
A. E. Cetin ◽  
T. C. Pearson ◽  
A. H. Tewfik
Keyword(s):  
Voice Recognition ◽  
Open Shell ◽  
Pistachio Nuts

scholarly journals Irreducible tensor form of three-particle operator for open-shell atoms

Open Physics ◽  
2011 ◽  
Vol 9 (3) ◽  
Author(s):  
Rytis Juršėnas ◽  
Gintaras Merkelis
Keyword(s):  
Angular Momentum ◽  
Symmetric Group ◽  
Open Shell ◽  
Matrix Elements ◽  
Irreducible Tensor ◽  
Tensor Form ◽  
Momentum Theory ◽  
Recoupling Coefficients ◽  
Particle Operator

AbstractA three-particle operator in a second quantized form is studied systematically and comprehensively. The operator is transformed into irreducible tensor form. Possible coupling schemes, identified by the classes of symmetric group S6, are presented. Recoupling coefficients that make it possible to transform a given scheme into another are produced by using the angular momentum theory combined with quasispin formalism. The classification of the three-particle operator which acts on n = 1, 2,..., 6 open shells of equivalent electrons of atom is considered. The procedure to construct three-particle matrix elements are examined.

Voice Recognition from Spectrograms: A Wavelet Based Approach

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 165-172 ◽  
Author(s):  
G. van de Wouwer ◽  
P. Scheunders ◽  
D. van Dyck ◽  
M. de Bodt ◽  
F. Wuyts ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Accuracy ◽  
Voice Recognition ◽  
Original Signal ◽  
Pattern Recognition Technique ◽  
Time Frequency ◽  
Novel Method ◽  
Recognition Technique

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 of voice dysphonia. Its performance is compared with another technique taken from the literature. A recognition accuracy of 98% is achieved for the classification between normal an dysphonic voices.

Classification of Pistachio Nuts Using a String Matching Technique

1996 ◽  
Vol 39 (3) ◽  
pp. 1197-1202 ◽  
Author(s):  
A. Ghazanfari ◽  
J. Irudayaraj
Keyword(s):  
String Matching ◽  
Pistachio Nuts ◽  
Matching Technique

scholarly journals Discrimination Between Closed and Open Shell (Turkish) Pistachio Nuts Using Undecimated Wavelet Packet Transform

2008 ◽  
Vol 1 (2) ◽  
pp. 159-172
Author(s):  
N. F. Ince ◽  
F. Goksu ◽  
A. H. Tewfik ◽  
I. Onaran ◽  
A. E. Cetin ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Open Shell ◽  
Wavelet Packet Transform ◽  
Pistachio Nuts ◽  
Undecimated Wavelet

Application of a Multi-Structure Neural Network (MSNN) to Sorting Pistachio Nuts

1997 ◽  
Vol 08 (01) ◽  
pp. 55-61 ◽  
Author(s):  
Ahmad Ghazanfari ◽  
Anthony Kusalik ◽  
Joseph Irudayaraj
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Back Propagation ◽  
Classification Performance ◽  
Fourier Descriptor ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Pistachio Nuts ◽  
The Individual

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptor of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.

scholarly journals Gaussian Membership Function used for Voice Recognition in Fuzzy Logic

2020 ◽  
Vol 8 (5) ◽  
pp. 2685-2689
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Group Membership ◽  
Voice Recognition ◽  
Age Group ◽  
Membership Functions ◽  
Human Voice ◽  
Degree Of Membership ◽  
Membership Value

Gaussian Membership function of a fuzzy set is a generalization form which is used to classify the human voice either based gender or age group. Membership functions were introduced by Zadeh in the first paper on fuzzy sets in the year 1965. In this paper we describe Gaussian membership function which we used to implement the simulation or classification of the human according to their age in fuzzy logic. A Gaussian Membership Function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1.

scholarly journals Classification of Arabic fricative consonants according to their places of articulation

2022 ◽  
Vol 12 (1) ◽  
pp. 936
Author(s):  
Youssef Elfahm ◽  
Nesrine Abajaddi ◽  
Badia Mounir ◽  
Laila Elmaazouzi ◽  
Ilham Mounir ◽  
...  
Keyword(s):  
Energy Distribution ◽  
Voice Recognition ◽  
Point Of View ◽  
Support Vector ◽  
Acoustic Cues ◽  
Classification Rate ◽  
Practical Point ◽  
Vector Machines ◽  
Speech Identification

<span>Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.</span>

scholarly journals Distributed acoustic cues for caller identity in macaque vocalization

2015 ◽  
Vol 2 (12) ◽  
pp. 150432 ◽  
Author(s):  
Makoto Fukushima ◽  
Alex M. Doyle ◽  
Matthew P. Mullarkey ◽  
Mortimer Mishkin ◽  
Bruno B. Averbeck
Keyword(s):  
Fundamental Frequency ◽  
Voice Recognition ◽  
Low Frequency ◽  
Acoustic Cues ◽  
Considerable Variability ◽  
Acoustic Features ◽  
Temporal Modulation ◽  
Multiple Features ◽  
Sweep Direction

Individual primates can be identified by the sound of their voice. Macaques have demonstrated an ability to discern conspecific identity from a harmonically structured ‘coo’ call. Voice recognition presumably requires the integrated perception of multiple acoustic features. However, it is unclear how this is achieved, given considerable variability across utterances. Specifically, the extent to which information about caller identity is distributed across multiple features remains elusive. We examined these issues by recording and analysing a large sample of calls from eight macaques. Single acoustic features, including fundamental frequency, duration and Weiner entropy, were informative but unreliable for the statistical classification of caller identity. A combination of multiple features, however, allowed for highly accurate caller identification. A regularized classifier that learned to identify callers from the modulation power spectrum of calls found that specific regions of spectral–temporal modulation were informative for caller identification. These ranges are related to acoustic features such as the call’s fundamental frequency and FM sweep direction. We further found that the low-frequency spectrotemporal modulation component contained an indexical cue of the caller body size. Thus, cues for caller identity are distributed across identifiable spectrotemporal components corresponding to laryngeal and supralaryngeal components of vocalizations, and the integration of those cues can enable highly reliable caller identification. Our results demonstrate a clear acoustic basis by which individual macaque vocalizations can be recognized.

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