Fractional Fourier transform based features for speaker recognition using support vector machine

Speech is a signal that includes speaker's emotion, characteristic specification, phonemeinformation etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.

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“…On the other hand, computational load is also increased. Another study which replaced the conventional DFT approach is [52].…”

Section: Robust Features Against Additive Noisementioning

confidence: 98%

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

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“…It is a linear operator which corresponds to the rotation of the signal between time and frequency plane, where time axis corresponds alpha=0 and frequency axis corresponds to alpha= π/2 [8], [9]. FRFT is more flexible and suitable for non-stationary signal as compared to fourier transform because of its orthonormal basis of chirp signals and degree of freedom of rotation of time frequency axis, [8], [9], [10], [14]. …”

Section: Fractional Fourier Transform Based Featuresmentioning

confidence: 99%

Fractional Fourier Transform Based Features for Musical Instrument Recognition Using Machine Learning Techniques

Bhalke

Rao

Bormane³

2014

Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013

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Abstract. This paper reports the result of Musical instrument recognition using fractional fourier transform (FRFT) based features. The FRFT features are computed by replacing conventional Fourier transform in Mel Frequecny Cepstral coefficient ( MFCC) with FRFT. The result of the system using FRFT is compared with the result of the system using Mel Frequency Cepstral Coefficients (MFCC), Wavelet and Timbrel features with different machine learning algorithms. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system using FRFT features outperforms over MFCC, Wavelet and Timbrel features with 91.84% recognition accuracy for individual instruments. The system is tested on benchmarked McGill University musical sound database. The experimental result shows that musical sound signals can be better represented using FRFT.

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“…The difference between Fourier transform (FT) [37] and its variant "fractional FT (FRFT) [38]", is that FRFT can analyze nonstationary signals, which FT cannot. Besides, FRFT transforms a particular signal into a unified time-frequency domain.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Zhang

Yang

et al. 2015

Entropy

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Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch's t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10ˆK-fold stratified cross validation test showed that this proposed "FRFE + WTT + TSVM" yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed "FRFE + WTT + TSVM" method is superior to 20 state-of-the-art methods.

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Fractional Fourier transform based features for speaker recognition using support vector machine

Cited by 24 publications

References 20 publications

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

Fractional Fourier Transform Based Features for Musical Instrument Recognition Using Machine Learning Techniques

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

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