A novel approach for speech feature extraction by Cubic-Log compression in MFCC

Devi, M. Ramya; Ravichandran, T.

doi:10.1109/icprime.2013.6496469

Cited by 13 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average classification accuracy (%) for various feature size (M) are listed in Table 3, where M = 16 gives the best result by the proposed scheme. The comparison results with MFCC (mel frequency ceptral coefficients) [13] features using the same training setup are presented in Table 4. The parameters for the MFCC are set as follows: MFCC window length = 20 ms (320 samples), number of MFCC features = 12, MFCC window overlapping = 50%.…”

Section: Food Anticipation 100mentioning

confidence: 99%

A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

Sattar

2022

The 1st International Online Conference on Agriculture—Advances in Agricultural Science and Technology

View full text Add to dashboard Cite

show abstract

Section: Food Anticipation 100mentioning

confidence: 99%

A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

Sattar

2022

The 1st International Online Conference on Agriculture—Advances in Agricultural Science and Technology

View full text Add to dashboard Cite

show abstract

“…Typical values for N and M are N ¼ 256. Depending upon sampling frequency rate have to construct the frame (Devi & Ravichandran, 2013;Martin & Juliet, 2010)…”

mentioning

confidence: 99%

Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Dessouky

El-Rashidy

Taha

et al. 2014

International Journal of Intelligent Computing in Medical Scien

View full text Add to dashboard Cite

Mel-Scale Frequency Cepstral Coefficients (MFCC) is very efficient technique for feature extraction. This paper proposes a Computer Aided Diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer Disease (AD) using MFCC technique for the 3-D MRI images. Classification is performed using Linear Support Vector Machine (SVM). Experimental results represent that the proposed CAD system using MFCC for AD recognition give excellent accuracy with small number of significant extracted features which reduces the memory size and simplify the hardware implementation of the CAD system. The proposed approach have better performance and stability.

show abstract

“…MFCC coefficients are a set of DCT decorrelated parameters, which are computed through a transformation of the logarithmically compressed filter-output energies, derived through a perceptually spaced triangular filter bank that processes the Discrete Fourier Transformed (DFT) speech signal. In cubic-log compression MFCC process instead of logtithmic energy in standard MFCC process, Cubic-log energy at each of the mel frequencies is taken [18].…”

Section: Fig 1: Speech Recognition System Modelmentioning

confidence: 99%

“…There are several methods has been proposed for feature extraction from speech signals like Perceptually Based Linear Predictive Analysis (PLP) [7], Linear Discriminant Analysis (LDA) [8], Linear Predictive Coding (LPC) Analysis [9] and Mel-Frequency Cepstrum Coefficients (MFCC) [10]- [18].…”

Section: Introductionmentioning

confidence: 99%

Speaker Independent Speech Recognition using MFCC with Cubic-Log Compression and VQ Analysis

Kaberpanthi¹,

Datar²

2014

IJCA

View full text Add to dashboard Cite

Speech processing is developed as one of the paramount requisition region of digital signal processing. Different fields for research in speech processing are speech recognition, speaker identification, speech bland, speech coding etc. The objective of Speaker Independent Speech Recognition is to concentrate, describe and distinguish information about speech signal and methodology towards creating the speaker free speech recognition system. Extracted information will be valuable for the directing and working different electronic contraptions and hardware through the human voice proficiently. Feature extraction is the first venture for speech recognition. Numerous algorithms are recommended / created by the scientists for feature extraction. In this work, the cubiclog compression in Mel-Frequency Cepstrum Coefficient (MFCC) feature extraction system is utilized to concentrate the characteristics from speech sign for outlining a speaker independent speaker recognition system. Extracted features are used to train and test this system with the help of Vector Quantization approach.

show abstract

A novel approach for speech feature extraction by Cubic-Log compression in MFCC

Cited by 13 publications

References 11 publications

A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Speaker Independent Speech Recognition using MFCC with Cubic-Log Compression and VQ Analysis

Contact Info

Product

Resources

About