Audio Emotion Recognition by Deep Neural Networks and Machine Learning Algorithms

Subramanian, R. Raja; Sireesha, Yalla; Reddy, Y.S.; Bindamrutha, Tavva; Harika, M.L.Teja; Sudharsan, R. Raja

doi:10.1109/icaeca52838.2021.9675492

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…When all the collected features were combined and classified, the lowest loss rate was 24.64%. Raja [11] conducted a study to confirm an effective data type when recognizing emotions using the Berlin dataset speech data. Speech data in 1D audio type was converted to a 2D image type by MFCC and used for training support vector machine (SVM), 1D-CNN, and 2D-CNN models.…”

Section: Related Work 21 Studies Applying Deep Learning To Spectrogra...mentioning

confidence: 99%

Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

Kim,

Kwak

2024

Applied Sciences

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This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The ability to interpret can be made more accurate by reducing uncertain learning data, applying data in different environments, and applying techniques that explain the reasoning behind the results. We designed a generalized model using three different datasets, and each speech was converted into a spectrogram image through STFT preprocessing. The spectrogram was divided into the time domain with overlapping to match the input size of the model. Each divided section is expressed as a Gaussian distribution, and the quality of the data is investigated by the correlation coefficient between distributions. As a result, the scale of the data is reduced, and uncertainty is minimized. VGGish and YAMNet are the most representative pretrained deep learning networks frequently used in conjunction with speech processing. In dealing with speech signal processing, it is frequently advantageous to use these pretrained models synergistically rather than exclusively, resulting in the construction of ensemble deep networks. And finally, various explainable models (Grad CAM, LIME, occlusion sensitivity) are used in analyzing classified results. The model exhibits adaptability to voices in various environments, yielding a classification accuracy of 87%, surpassing that of individual models. Additionally, output results are confirmed by an explainable model to extract essential emotional areas, converted into audio files for auditory analysis using Grad CAM in the time domain. Through this study, we enhance the uncertainty of activation areas that are generated by Grad CAM. We achieve this by applying the interpretable ability from previous studies, along with effective preprocessing and fusion models. We can analyze it from a more diverse perspective through other explainable techniques.

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Section: Related Work 21 Studies Applying Deep Learning To Spectrogra...mentioning

confidence: 99%

Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

Kim,

Kwak

2024

Applied Sciences

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“…In recent years, automatic emotion detection has been applied in various areas such as emotion recognition from movies [ 4 ], audio [ 5 ], text [ 6 ], and facial expressions [ 7 ]. With the development of low-cost wearable technology, non-invasive electroencephalography (EEG)-based techniques for automatic emotion identification have achieved widespread popularity and acceptance [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures

Tripathi

Sharma

2023

Sensors

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Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.

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LeafNet: Design and Evaluation of a Deep CNN Model for Recognition of Diseases in Plant Leaves

Subramanian,

Devi,

Tulasi

et al. 2024

Applications of Computer Vision and Drone Technology in Agriculture 4.0

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Audio Emotion Recognition by Deep Neural Networks and Machine Learning Algorithms

Cited by 9 publications

References 7 publications

Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures

LeafNet: Design and Evaluation of a Deep CNN Model for Recognition of Diseases in Plant Leaves

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