Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet, Saddam; Alghamdi, Abdullah M.; Taj-Eddin, Islam F.

doi:10.11591/ijece.v12i2.pp2066-2078

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…Talafha et al [13] used the LSTM layer as a decoder on RNN architecture, the proposed method uses the CNN layer and CNN-LSTM layer as a decoder on LSTM architecture [14], [15]. After each CNN layer, [16] uses the max pool layer to reduce the overfitting/underfitting. With the same goal, the proposed method CNN-LSTM uses a dropout layer after each CNN layer.…”

Section: Introductionmentioning

confidence: 99%

Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers

Sams

Zahra²

2023

Bulletin EEI

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Music carries emotional information and allows the listener to feel the emotions contained in the music. This study proposes a multimodal music emotion recognition (MER) system using Indonesian song and lyrics data. In the proposed multimodal system, the audio data will use the mel spectrogram feature, and the lyrics feature will be extracted by going through the tokenizing process from XLNet. Convolutional long short term memory network (CNN-LSTM) performs the audio classification task, while XLNet transformers performs the lyrics classification task. The outputs of the two classification tasks are probability weight and actual prediction with the value of positive, neutral, and negative emotions, which are then combined using the stacking ensemble method. The combined output will be trained into an artificial neural network (ANN) model to get the best probability weight output. The multimodal system achieves the best performance with an accuracy of 80.56%. The results showed that the multimodal method of recognizing musical emotions gave better performance than the single modal method. In addition, hyperparameter tuning can affect the performance of multimodal systems.

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Section: Introductionmentioning

confidence: 99%

Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers

Sams

Zahra²

2023

Bulletin EEI

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“…where x(n) and 𝑥 ̃ are speech samples and their estimates, and a(k) is the feature vector of LPC coefficients where p is the linear predictive filter order. The autocorrelation function (ACF) of each frame signal can be computed [10] using (6).…”

Section: Linear Prediction Coefficientsmentioning

confidence: 99%

“…The urge of identifying gender can be demonstrated in many situations. Since the emergence of human-machine interaction (HMI), many fields require machines to identify gender for numerous modern applications [4]- [6]. Past research on voice pathology shows that it is gender biased.…”

Section: Introductionmentioning

confidence: 99%

Speech-based gender recognition using linear prediction and mel-frequency cepstral coefficients

Ali

Noorsal

Mokhtar

et al. 2022

IJEECS

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Gender discrimination and awareness are essentially practiced in social, education, workplace, and economic sectors across the globe. A person manifests this attribute naturally in gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic in speech recognition systems that can be found in many speech technology applications. However, retrieving salient gender-related information from a speech signal is a challenging problem since speech contains abundant information apart from gender. The paper intends to compare the performance of human vocal tract-based model i.e., linear prediction coefficients (LPC) and human auditory-based model i.e., Mel-frequency cepstral coefficients (MFCC) which are popularly used in other speech recognition tasks by experimentation of optimal feature parameters and classifier’s parameters. The audio data used in this study was obtained from 93 speakers uttering selected words with different vowels. The two feature vectors were tested using two classification algorithms namely, discriminant analysis (DA) and artificial neural network (ANN). Although the experimental results were promising using both feature parameters, the best overall accuracy rate of 97.07% was recorded using MFCC-ANN techniques with almost equal performance for male and female classes.

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“…It took four layers to get an accuracy of 8.759 % [19]. This method has applications in forensic medicine, where it integrates face measuring for images with deep learning at a pace of more than 3700 images per second with an accuracy of 89 % [20]. Using deep learning in face detection in the MATLAB program by training the convolutional neural network and achieving 100 % accuracy, and facial emotions were recognized using wavelets (DCHWT) and convolved using a neural network known as the Chebyshev wavelet (CHW) convolutional neural network [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Improvement of color image analysis using a hybrid artificial intelligence algorithm

Muhi-Aldeen,

Mahmood,

Abdulrahman

et al. 2024

Eureka: PE

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Large communications of voice and images over the Internet, which leads to limited space for very large data during the aforementioned correspondence, to overcome this issue to maintain the quality of this technology. The proposal in the efficient algorithm in this work is a method to derive the two new filters from the second and third Chebyshev polynomials by forming the discrete wavelets with the mother wavelet to be used in image processing in order to overcome the problem mentioned above due to the correspondence, The filters that were derived are Filter Discrete Second Chebyshev Wavelets Transform (FDSCWT) and Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT)to process the image by analysis, noise reduction, and image compression. Many of the techniques previously used in the field of image processing do not preserve image information during processing, but when using the new technology proposed in this work, it has been proven to preserve the image with its important information and data through the readings obtained shown in the tables below. These readings are average. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR) in preprocessing. After the initial processing stage, the deep learning stage begins in the field of artificial intelligence. A (CNN) is trained with the two new filters to be the first Discrete Second Chebyshev Wavelets (DSCWCNN) and the second Discrete Third Chebyshev Wavelets Convolutional Neural Network (DTCWCNN), with the code being generated in the MATLAB program with a network Alex Net to complete the classification process that was added in this work to implement the recognition technology. Faces detection with new filters in deep learning to be a unique experience to reach a high level of accuracy of 98.60 % with the network for the filter DSCWCNN and 98.92 % with the network for the filter DTCWCNN in a very short time, which will be mentioned later in the work

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Gender recognition from unconstrained selfie images: a convolutional neural network approach

Cited by 7 publications

References 37 publications

Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers

Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers

Speech-based gender recognition using linear prediction and mel-frequency cepstral coefficients

Improvement of color image analysis using a hybrid artificial intelligence algorithm

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