A bio-inspired emotion recognition system under real-life conditions

Chenchah, Farah; Lachiri, Zied

doi:10.1016/j.apacoust.2016.06.020

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…Chenchah, F. argued that in real life, combining the development of emotion recognition with the development of speech features can improve the efficiency of emotion recognition. Chenchah, F. proposed and introduced a feature extraction method based on spectral features in speech emotion recognition applications [19]. Spilka, M. J. pointed out that the relationship between endogenous intranasal oxytocin and social cognition in schizophrenia is poorly understood by the general public.…”

Section: Introductionmentioning

confidence: 99%

Research on algorithm composition and emotion recognition based on adaptive networks

Hou,

Wang,

2023

Applied Mathematics and Nonlinear Sciences

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Adaptive linear neural networks lay the foundation for the development of the uniqueness of algorithmic composition and emotion recognition. In this paper, we first analyze the process of emotion recognition and the development of algorithmic compositions to establish the emotion recognition dataset. Secondly, the algorithm of the adaptive linear neural network is selected, including the analysis of the adaptive linear neuron model and gradient and most rapid descent method and LMS algorithm. The analysis focuses on the LMS algorithm flow, convergence conditions and performance parameters of the LMS algorithm. Finally, the sentiment recognition results of four models, SVM, CNN, LSTM and Adaline neural network, based on different dimensional self-encoder features, are analyzed. To verify whether the classification method of self-encoder + Adaline neural network can find the information connection between various emotions and improve the efficiency of emotion recognition. The classification method of self-encoder + Adaline neural network can improve the recognition rate by up to 85% for noise-reducing self-encoder features in 500 dimensions.

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Section: Introductionmentioning

confidence: 99%

Research on algorithm composition and emotion recognition based on adaptive networks

Hou,

Wang,

2023

Applied Mathematics and Nonlinear Sciences

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“…We increase the levels of each dimension in order to describe more emotions. We use Long Short Term Memory (LSTM) network as a classifier, in which the feature set is Mel-Frequency Cepstrum Coefficients (MFCCs) [25]. Furthermore, we evaluate a Low-Level Descriptors (LLDs) feature set and basic MFCCs feature set to test how feature sets influence the speech emotion recognition system.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Effect of Increased Dimension Levels in Speech Emotion Recognition

Wang

Zhao

2022

IEEE Access

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In human-machine interaction systems, speech emotion recognition plays a key role. Recognition of categorical emotions has made a great improvement during the last few decades, but emotion recognition of spontaneous speech is still very challenging. This paper aims to investigate emotion recognition from the spontaneous speech in the three-dimensional model. Each dimension represents one primitive, generic attribute of an emotion. Middle levels of each dimension were introduced in this paper. LSTM network was employed to estimate the dimensions due to its effectiveness in speech emotion recognition. In the experiments, we use the IEMOCAP database and the accuracy is 30-35%. The confusion matrixes show that our method leads to a more concentrated dimension location. Furthermore, dimensions were applied in categorical emotion recognition. This indicates that increasing dimension levels could provide a possibility of dimension estimation, and suggests that it is possible to promote speech emotion recognition with dimensions.

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“…MFCC is calculated by performing a discrete cosine transformation on the output from triangular filter banks evenly spaced along a logarithmic axis; this is referred to as a mel scale, and it approximates the human auditory frequency response. PNCC is a feature value developed to improve the robustness of voice recognition systems in noisy environments [11][12][13][14]. Because BS captured using noncontact microphones are generally low in volume and have degraded SNR, PNCC can be expected to be effective; it improves the process of calculating MFCC to make it more similar to certain physiological aspects of humans.…”

Section: Automatic Bs Extraction On the Basis Of Acoustic Featuresmentioning

confidence: 99%

“…The proposed method is primarily made up of the following four steps: (1) segment detection using the short-term energy (STE) method; (2) automatic extraction of two acoustic features-mel-frequency cepstral coefficients (MFCC) [9,10] and power-normalized cepstral coefficients (PNCC) [11][12][13][14]-from segments; (3) automatic classification of segments as BS/non-BS based on an artificial neural network (ANN); and (4) evaluation of bowel motility on the basis of the acoustic features in the time domain of the BS that were automatically extracted. On the basis of audio data recorded from 20 human participants before and after they consumed carbonated water, we verified (i) the validity of automatic BS extraction by the proposed method and (ii) the validity of bowel motility evaluation based on acoustic features in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

et al. 2018

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Information on bowel motility can be obtained via magnetic resonance imaging (MRI)s and X-ray imaging. However, these approaches require expensive medical instruments and are unsuitable for frequent monitoring. Bowel sounds (BS) can be conveniently obtained using electronic stethoscopes and have recently been employed for the evaluation of bowel motility. More recently, our group proposed a novel method to evaluate bowel motility on the basis of BS acquired using a noncontact microphone. However, the method required manually detecting BS in the sound recordings, and manual segmentation is inconvenient and time consuming. To address this issue, herein, we propose a new method to automatically evaluate bowel motility for noncontact sound recordings. Using simulations for the sound recordings obtained from 20 human participants, we showed that the proposed method achieves an accuracy of approximately 90% in automatic bowel sound detection when acoustic feature power-normalized cepstral coefficients are used as inputs to artificial neural networks. Furthermore, we showed that bowel motility can be evaluated based on the three acoustic features in the time domain extracted by our method: BS per minute, signal-to-noise ratio, and sound-to-sound interval. The proposed method has the potential to contribute towards the development of noncontact evaluation methods for bowel motility.

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A bio-inspired emotion recognition system under real-life conditions

Cited by 15 publications

References 17 publications

Research on algorithm composition and emotion recognition based on adaptive networks

Research on algorithm composition and emotion recognition based on adaptive networks

Investigation of the Effect of Increased Dimension Levels in Speech Emotion Recognition

Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

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