Emotion-driven Chinese folk music-image retrieval based on DE-SVM

Xing, Baixi; Zhang, Kejun; Sun, Shouqian; Zhang, Lekai; Gao, Zenggui; Wang, Jiaxi; Chen, Shi

doi:10.1016/j.neucom.2014.08.007

Cited by 31 publications

(20 citation statements)

References 27 publications

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“…The back-propagation architecture is the most popular model for complex, multi-layered networks [49] and has been widely used in pattern recognition models for sEMG signals [50]. To set up the BPNN model, we used a three-layer network consisting of one input layer, one hidden layers with a sigmoid function, and one output layer with a tanh function.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Eyebrow emotional expression recognition using surface EMG signals

2015

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Section: Back Propagation Neural Networkmentioning

confidence: 99%

Eyebrow emotional expression recognition using surface EMG signals

2015

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“…Back propagation (BP) learning comprises two processing steps involving the forward and BP of error. The BP architecture is the most popular model for complex, multi-layered networks (Xing et al, 2015 ). A three-layer network consisting of one input layer, one hidden layer with a sigmoid function, and one output layer with a tanh function was used to set up the BPNN classification models.…”

Section: Methodsmentioning

confidence: 99%

Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

Yang

Chen

2016

Front. Neurosci.

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Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we propose a method in which 11 basic one-stroke sketching shapes are identified from the sEMG signals generated by the forearm and upper arm muscles from 4 subjects. Time domain features such as integrated electromyography, root mean square and mean absolute value were extracted with analysis windows of two length conditions for pattern recognition. After reducing data dimensionality using principal component analysis, the shapes were classified using Gene Expression Programming (GEP). The performance of the GEP classifier was compared to the Back Propagation neural network (BPNN) and the Elman neural network (ENN). Feature extraction with the short analysis window (250 ms with a 250 ms increment) improved the recognition rate by around 6.4% averagely compared with the long analysis window (2500 ms with a 2500 ms increment). The average recognition rate for the eleven basic one-stroke sketching patterns achieved by the GEP classifier was 96.26% in the training set and 95.62% in the test set, which was superior to the performance of the BPNN and ENN classifiers. The results show that the GEP classifier is able to perform well with either length of the analysis window. Thus, the proposed GEP model show promise for recognizing sketching based on sEMG signals.

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“…A retrieval system between Chinese folk images and Chinese folk music based on a differential evolutionary-support vector machine (DE-SVM) has been proposed. 14 In another study, a music emotion recognition system based on modified gene expression programming (GEP) was developed.The performance of the system was reported as being better than ordinary GEP. 15 A model for music emotion recognition using feature selection and statistical models has been proposed.…”

Section: Related Workmentioning

confidence: 99%

Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

Sarno

Ridoean

Sunaryono

et al. 2018

Int. J. Artif. Intell. Tools

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Psychologically, music can affect human mood and influence human behavior. In this paper, a novel method for music mood classification is introduced. In the experiment, music mood classification was performed using feature extraction based on MPEG-7 features from the ISO/IEC 15938 standard for describing multimedia content. The result of this feature extraction are 17 low-level descriptors. Here, we used the Audio Power, Audio Harmonicity, and Audio Spectrum Projection features. Moreover, the discrete wavelet transform (DWT) was utilized for audio signal reconstruction. The reconstructed audio signals were classified by the new method, which uses a support vector machine with a confidence interval (SVM-CI). According to the experimental results, the success rate of the proposed method was satisfactory and SVM-CI outperformed the ordinary SVM.

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Emotion-driven Chinese folk music-image retrieval based on DE-SVM

Cited by 31 publications

References 27 publications

Eyebrow emotional expression recognition using surface EMG signals

Eyebrow emotional expression recognition using surface EMG signals

Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

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