Robust cepstral feature for bird sound classification

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.

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Respiratory sound classification using cepstral features and support vector machine

2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS) ◽

10.1109/raics.2013.6745460 ◽

2013 ◽

Cited By ~ 5

Author(s):

Rajkumar Palaniappan ◽

K. Sundaraj

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Respiratory Sound ◽

Sound Classification ◽

Cepstral Features

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Classification of lung sounds based on linear prediction cepstral coefficients and support vector machine

2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) ◽

10.1109/aeect.2015.7360527 ◽

2015 ◽

Cited By ~ 1

Author(s):

Mohamed Moustafa Azmy

Keyword(s):

Support Vector Machine ◽

Linear Prediction ◽

Support Vector ◽

Lung Sounds ◽

Linear Prediction Cepstral Coefficients ◽

Cepstral Coefficients

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

Journal of Engineering and Applied Sciences (JEAS) University of Engineering and Technology Peshawar ◽

10.17582/journal.jeas/39.1.116.126 ◽

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

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Gearbox Fault Diagnosis Based on Mel-Frequency Cepstral Coefficients and Support Vector Machine

Computational Intelligence and Its Applications - IFIP Advances in Information and Communication Technology ◽

10.1007/978-3-319-89743-1_20 ◽

2018 ◽

pp. 220-231 ◽

Cited By ~ 2

Author(s):

Tarak Benkedjouh ◽

Taha Chettibi ◽

Yassine Saadouni ◽

Mohamed Afroun

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Mel Frequency Cepstral Coefficients ◽

Cepstral Coefficients

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Dimensionality reduction for voice disorders identification system based on Mel Frequency Cepstral Coefficients and Support Vector Machine

2015 7th International Conference on Modelling, Identification and Control (ICMIC) ◽

10.1109/icmic.2015.7409479 ◽

2015 ◽

Cited By ~ 8

Author(s):

Nawel Souissi ◽

Adnane Cherif

Keyword(s):

Support Vector Machine ◽

Dimensionality Reduction ◽

Voice Disorders ◽

Support Vector ◽

Identification System ◽

Mel Frequency Cepstral Coefficients ◽

Cepstral Coefficients

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

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37545 ◽

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

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Acoustic comparison of electronics disguised voice using Different semitones

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.16.11502 ◽

2018 ◽

Vol 7 (2.16) ◽

pp. 98 ◽

Cited By ~ 2

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.

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