scholarly journals EEG Based Four Class Human Limb Movement Detection by Mel Frequency Cepstral Coefficients and Quadratic Multi-Class Support Vector Machine

2020 ◽  
Vol 39 (1) ◽  
Author(s):  
Nasir Rashid ◽  
Javaid Iqbal ◽  
Umar Shahbaz Khan ◽  
Mohsin Islam Tiwana ◽  
Amir Hamza
Keyword(s):  
Support Vector Machine ◽  
Limb Movement ◽  
Support Vector ◽  
Movement Detection ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients ◽  
Human Limb

scholarly journals Robust cepstral feature for bird sound classification

2022 ◽  
Vol 12 (2) ◽  
pp. 1477
Author(s):  
Murugaiya Ramashini ◽  
P. Emeroylariffion Abas ◽  
Kusuma Mohanchandra ◽  
Liyanage C. De Silva
Keyword(s):  
Support Vector Machine ◽  
Wildlife Management ◽  
Linear Prediction ◽  
Environmental Indicators ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Visual Identification ◽  
Cepstral Features ◽  
Cepstral Coefficients

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management.

scholarly journals Emotion Recognition in Speech Using with SVM, DSVM and Auto-Encoder

2021 ◽  
Vol 9 (8) ◽  
pp. 1021-1026
Author(s):  
Jeena Augustine
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Emotion Recognition ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Classification Rate ◽  
Signal Process ◽  
Common Technique ◽  
Audio Information ◽  
Deep Support

Abstract: Emotions recognition from the speech is one of the foremost vital subdomains within the sphere of signal process. during this work, our system may be a two-stage approach, particularly feature extraction, and classification engine. Firstly, 2 sets of options square measure investigated that are: thirty-nine Mel-frequency Cepstral coefficients (MFCC) and sixty-five MFCC options extracted supported the work of [20]. Secondly, we've got a bent to use the Support Vector Machine (SVM) because the most classifier engine since it is the foremost common technique within the sector of speech recognition. Besides that, we've a tendency to research the importance of the recent advances in machine learning along with the deep kerne learning, further because the numerous types of auto-encoders (the basic auto-encoder and also the stacked autoencoder). an oversized set of experiments unit conducted on the SAVEE audio information. The experimental results show that the DSVM technique outperforms the standard SVM with a classification rate of sixty-nine. 84% and 68.25% victimization thirty-nine MFCC, severally. To boot, the auto encoder technique outperforms the standard SVM, yielding a classification rate of 73.01%. Keywords: Emotion recognition, MFCC, SVM, Deep Support Vector Machine, Basic auto-encoder, Stacked Auto encode

scholarly journals Acoustic comparison of electronics disguised voice using Different semitones

2018 ◽  
Vol 7 (2.16) ◽  
pp. 98 ◽  
Author(s):  
Mahesh K. Singh ◽  
A K. Singh ◽  
Narendra Singh
Keyword(s):  
Support Vector Machine ◽  
Acoustic Analysis ◽  
Speaker Identification ◽  
Support Vector ◽  
Acoustic Features ◽  
Acoustic Feature ◽  
Mel Frequency Cepstral Coefficients ◽  
Identification Rate ◽  
Normal Voice ◽  
Feature Based

This paper emphasizes an algorithm that is based on acoustic analysis of electronics disguised voice. Proposed work is given a comparative analysis of all acoustic feature and its statistical coefficients. Acoustic features are computed by Mel-frequency cepstral coefficients (MFCC) method and compare with a normal voice and disguised voice by different semitones. All acoustic features passed through the feature based classifier and detected the identification rate of all type of electronically disguised voice. There are two types of support vector machine (SVM) and decision tree (DT) classifiers are used for speaker identification in terms of classification efficiency of electronically disguised voice by different semitones.  

Speech Recognition Based on Feature Extraction with the Aid of Multi Support Vector Machine

2016 ◽  
Vol 13 (10) ◽  
pp. 6616-6627
Author(s):  
B Kanisha ◽  
G Balakrishnan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Back Propagation ◽  
Spectral Feature ◽  
Signal Frequency ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Proper Form ◽  
Feed Forward Back Propagation

Speech recognition process applications are emerging as ever-zooming and efficient mechanisms in the hi-tech universe. There is a host of diverse interactive speech-aware applications in the market. With the rocketing requirement for upcoming embedded platforms and with the incredible increase in the demand for embedded computing, it is highly indispensable that the speech recognition systems (SRS) are put in place at the right time and in the proper form so that it is easily possible to perform multimedia tasks on these mechanisms. In this work, primarily through preprocessing the speech signal is processed where for the recognition of the particular signal, the noise is detached and then it enters into feature extraction in that peak signal frequency and it is compared with the standard signal and recognized. The signal is processed and noise free signal is produced by processing the signal to Mel frequency cepstral coefficients (MFCC), Tri-spectral feature, and discrete wave transform (DWT). To the input of the multi-class Support vector machine, the output of the above mentioned features is given. The processed signal is converted in to text by multi SVM. It is proved that our proposed technique is better than the existing technique by comparing the existing technique (FFBN) feed forward back propagation with the proposed technique. The proposed technique is implemented in the working platform of MATLAB.

scholarly journals Vowel recognition of patients after total laryngectomy using Mel Frequency Cepstral Coefficients and mouth contour

2010 ◽  
Vol 20 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Rafal Pietruch ◽  
Antoni Grzanka
Keyword(s):  
Support Vector Machines ◽  
Total Laryngectomy ◽  
Vocal Tract ◽  
Recognition Rate ◽  
Video Signal ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Vector Machines ◽  
Cepstral Coefficients

The paper addresses a problem of isolated vowels recognition in patients following total laryngectomy. The visual and acoustic speech modalities were separately incorporated in the machine learning algorithms. The authors used the Mel Frequency Cepstral Coefficients as acoustic descriptors of a speech signal. A lip contour was extracted from a video signal of the speaking faces using OpenCV software library. In a vowels recognition procedure the three types of classifiers were used for comparison purposes: Artificial Neural Networks, Support Vector Machines and Naive Bayes. The highest recognition rate was evaluated using Support Vector Machines. For a group of the laryngectomees having a different quality of speech the authors achieved 75% for acoustic and 40% for visual recognition performances. The authors obtained higher recognition rate than in a previous research where 10 cross-sectional areas of a vocal tract were estimated. Using presented image processing algorithm the visual features can be extracted automatically from a video signal.

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