Gender Classification Based on FeedForward Backpropagation Neural Network

Azghadi, S. Mostafa Rahimi; Bonyadi, Mohammad Reza; Shah‐Hosseini, Hamed

doi:10.1007/978-0-387-74161-1_32

Cited by 15 publications

(3 citation statements)

References 4 publications

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“…Traditionally, signal processing has been applied to other fields such as gender identification, emotion classification and speech recognition. In the gender identification field, Melfrequency cepstral coefficients (MFCCs) and pitch frequencies are acoustic features that have been used to identify a speaker's gender [1,2,3,4,5]. The duration of speech segments has been studied to determine a speaker's gender [6].…”

Section: Applied Mechanics and Materialsmentioning

confidence: 99%

Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network

Khunarsa

Mahawan

Nakjai

et al. 2016

AMM

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The challenging for buyers around the globe to identify good quality of Durian. For several kinds of Durian, it may be difficult for buyers to determine the Durian quality by appearance. The ability to select only good quality Durian without cutting or cleaving is useful because buyers will not waste money ordering undesirable Durian.This paper proposes a nondestructive technique to determine the stages of maturity of durian fruits. The presented methodology utilizes the concept of pattern matching. We used the local knocking equipment to knock the durian for knocked-sound. After that the knocked-sound was analyzed and generated to Mel-frequency cepstral coefficients (MFCCs) that is used to train data for the classifier. Feed-Forward Neural Network was used for the classifier and can effectively classification the stages of maturity of durian fruits with accuracy rate more than 82%.

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Section: Applied Mechanics and Materialsmentioning

confidence: 99%

Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network

Khunarsa

Mahawan

Nakjai

et al. 2016

AMM

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“…In the emotion recognition field, combined acoustic features including fundamental frequencies, spectral features, energy features and their augmentations have been studied to classify emotional states from human spoken signals [9]. In gender classification, pitch contours, fundamental frequencies and MFCCs have also been widely used to identify the gender of speakers [10][11][12][13]. The short duration of human speech segments has also been explored for gender distinction [14].…”

Section: Applied Mechanics and Materialsmentioning

confidence: 99%

Computerized Unripe and Ripe Durian Striking Sound Recognition Using Syllable-Based HMMs

Phoophuangpairoj

2013

AMM

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Buying expensive agricultural produce and fruit such as durians that are unripe can result in a bad experience for a consumer and a loss in profit for a retailer. Therefore, the study of durian striking sounds to create an automatic method of recognizing the ripeness of durians without cutting or damaging them is interesting because it could benefit shoppers and the fruit industry. To solve the problem, the following method of recognizing unripe and ripe striking signals is proposed. First, in the recognition process, the spectral features of the signals are extracted. Then, acoustic models of durian striking sounds are created using syllable-based Hidden Markov Models (HMM). Finally, sequences of syllable-based unripe and ripe durians and defined possible durian ripeness results are applied to recognize the ripeness. Average ripeness recognition rates of more than 90.0% were achieved when using any number of strikes from one to five. When the number of strikes was limited to four and five, higher recognition rates of 95.0% and 92.0% were achieved for the untrained and unknown test sets, respectively. An average total recognition time of less than 80 milliseconds was taken to recognize the unripe and ripe durian striking sounds. The experimental results indicate that the proposed method is a time-efficient, accurate and effective way of recognizing durian ripeness.

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“…Harb and Chen (2005) used pitch and spectral features with multi layer perceptron classifier and reported 93% of classification accuracy [3]. The performance achieved by Azghadi and Bonyadi (2007) was 96% [4]. Some authors used HMM 2 as classifier.…”

Section: Introductionmentioning

confidence: 96%

Performance improvement in automatic gender identification using hierarchical clustering

Keyvanrad

Homayounpour

2010

2010 5th International Symposium on Telecommunications

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In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature selection to reduce clustering and classification run time. Another idea used in this paper is to choose and use the best classifier in each level of the proposed hierarchical gender classifier. Three classifiers including GMM, SVM and MLP are used. 96.19% gender classification accuracy was obtained on OGI Multilanguage Telephony Corpus which is 3.5%, 2.5%, and 1% better than the performance obtained by traditional classifiers using GMM, SVM and MLP respectively.

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Gender Classification Based on FeedForward Backpropagation Neural Network

Cited by 15 publications

References 4 publications

Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network

Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network

Computerized Unripe and Ripe Durian Striking Sound Recognition Using Syllable-Based HMMs

Performance improvement in automatic gender identification using hierarchical clustering

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