Cryptobiometrics for the Generation of Cancellable Symmetric and Asymmetric Ciphers with Perfect Secrecy

Vera, Vicente; Ávila, Carmen Sánchez

doi:10.3390/math8091536

Cited by 3 publications

(2 citation statements)

References 94 publications

(85 reference statements)

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“…For example, Kwon et al [11] suggested a model that focuses more on the sentiment information of the raw data, and they sampled from the raw data as the input to the model. Jara-Vera et al [12] instead used a specific representation of the audio recording as input to the model. Following the extensive expansion of the deep learning technology in the domain of speech sentiment analysis, researchers can extract higher-level features from speech signals and can obtain better accuracy and recognition rates compared to lower-level features [13].…”

Section: Introductionmentioning

confidence: 99%

Speech Sentiment Analysis Using Hierarchical Conformer Networks

2022

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Multimodality has been widely used for sentiment analysis tasks, especially for speech sentiment analysis. Compared with the emotion expression of most text languages, speech is more intuitive for human emotion, as speech contains more and richer emotion features. Most of the current studies mainly involve the extraction of speech features, but the accuracy and prediction rate of the models still need to be improved. To improve the extraction and fusion of speech sentiment feature information, we present a new framework. The framework adopts a hierarchical conformer model and an attention-based GRU model to increase the accuracy of the model. The method has two main parts: a local feature learning group and a global feature learning group. The local feature learning group is mainly used to learn the spatio-temporal feature information of speech emotion features through the conformer model, and a combination of convolution and transformer is used to be able to enhance the extraction of long and short-term feature information. The global features are then extracted by the AUGRU model, and the fusion of features is performed by the attention mechanism to access the weights of feature information. Finally, the sentiment is identified by a fully connected network layer, and then classified by a central loss function and a softmax function. Compared with existing speech sentiment analysis models, we obtained better sentiment classification results on the IEMOCAP and RAVDESS benchmark datasets.

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Section: Introductionmentioning

confidence: 99%

Speech Sentiment Analysis Using Hierarchical Conformer Networks

2022

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“…These models are fundamentally different in nature. For example, one group designed a DNN model that detects significant cues from raw audio samples [2], and another group utilized a particular representation of an audio recording to provide input for the model [12]. In order to make a robust and a significant model, the researchers utilized different types of feature combinations using diverse network strategies.…”

Section: Introductionmentioning

confidence: 99%

CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network

Mustaqeem

Kwon

2020

Mathematics

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Artificial intelligence, deep learning, and machine learning are dominant sources to use in order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) system is a basic necessity and an emerging research area of digital audio signal processing. However, SER plays an important role with many applications that are related to human–computer interactions (HCI). The existing state-of-the-art SER system has a quite low prediction performance, which needs improvement in order to make it feasible for the real-time commercial applications. The key reason for the low accuracy and the poor prediction rate is the scarceness of the data and a model configuration, which is the most challenging task to build a robust machine learning technique. In this paper, we addressed the limitations of the existing SER systems and proposed a unique artificial intelligence (AI) based system structure for the SER that utilizes the hierarchical blocks of the convolutional long short-term memory (ConvLSTM) with sequence learning. We designed four blocks of ConvLSTM, which is called the local features learning block (LFLB), in order to extract the local emotional features in a hierarchical correlation. The ConvLSTM layers are adopted for input-to-state and state-to-state transition in order to extract the spatial cues by utilizing the convolution operations. We placed four LFLBs in order to extract the spatiotemporal cues in the hierarchical correlational form speech signals using the residual learning strategy. Furthermore, we utilized a novel sequence learning strategy in order to extract the global information and adaptively adjust the relevant global feature weights according to the correlation of the input features. Finally, we used the center loss function with the softmax loss in order to produce the probability of the classes. The center loss increases the final classification results and ensures an accurate prediction as well as shows a conspicuous role in the whole proposed SER scheme. We tested the proposed system over two standard, interactive emotional dyadic motion capture (IEMOCAP) and ryerson audio visual database of emotional speech and song (RAVDESS) speech corpora, and obtained a 75% and an 80% recognition rate, respectively.

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HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme

Latha

Ramaprasath

2023

Journal of Intelligent Systems

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Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.

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Cryptobiometrics for the Generation of Cancellable Symmetric and Asymmetric Ciphers with Perfect Secrecy

Cited by 3 publications

References 94 publications

Speech Sentiment Analysis Using Hierarchical Conformer Networks

Speech Sentiment Analysis Using Hierarchical Conformer Networks

CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network

HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme

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