Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single ...generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perform multi-stage enhancement mapping, which gradually refines the noisy input signals in a stage-wise fashion. Furthermore, we study two scenarios: (1) the generators share their parameters and (2) the generators' parameters are independent. The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint. On the contrary, the latter allows the generators to flexibly learn different enhancement mappings at different stages of the network at the cost of an increased model size. We demonstrate that the proposed multi-stage enhancement approach outperforms the one-stage SEGAN baseline, where the independent generators lead to more favorable results than the tied generators. The source code is available at http://github.com/pquochuy/idsegan .
Dysarthric speech is the noisy or source distortion speech. Reasonable speech enhancement is required to obtain higher communication quality for non-stationary noises. Owing to complexities in speech ...rate of dysarthric persons, understanding their speech is more critical and complex task. The generic recognition systems do not perform well in speech recognition. Hence, this paper proposes a Fractional Competitive Crow Search Algorithm-based Speech Enhancement Generative Adversarial Network (FCCSA-SEGAN) for enhancing the speech signal. Initially, at the pre-processing stage, the noise from the speech signal is removed using spectral subtraction method. Then, pre-processed signal is fed to speech enhancement, where signal quality is enhanced by the Speech Enhancement Generative Adversarial Network (SEGAN), which is trained by the developed FCCA. By the incorporation of Fractional Calculus (FC) and Competitive Crow Search Algorithm (CSSA), proposed FCCA is obtained, in which CSSA is hybridization of Crow Search Algorithm (CSA) and Competitive Swarm Optimizer (CSO). After that, the features, such as Multiple Kernel Weighted Mel Frequency Cepstral Coefficient (MKMFCC), Linear Prediction Cepstral Coefficient (LPCC), spectral flux, spectral crest, spectral centroid, and pitch chroma are extracted. Moreover, to increase the dimensionality of signal samples, noises are added to the original signal through data augmentation phase. Finally, using Competitive Crow Search Algorithm-based Hierarchical Attention Network (CCSA-based HAN), speech recognition process is done. In addition, the performance of the proposed method is evaluated using the UA speech database and the accuracy, sensitivity, and specificity of 0.930, 0.933, and 0.934 are obtained by the proposed method. By the proposed speech enhancement approach, higher Perceptual Evaluation of Speech Quality (PESQ) and lower Root Mean Square Error (RMSE) of 3.14, and 0.022 are attained.
Segmentation and classification of brain tumor are time-consuming and challenging chore in clinical image processing. Magnetic Resonance Imaging (MRI) offers more information related to human soft ...tissues that assists in diagnosing brain tumor. Precise segmentation of the MRI images is vital to diagnose brain tumor by means of computer-aided medical tools. Afterwards suitable segmentation of MRI brain tumor images, tumor classification is performed that is a hard chore owing to complications. Therefore, Gannet Aquila Optimization Algorithm_deep maxout network (GAOA_DMN) and GAOA_K-Net+speech enhancement generative adversarial network (GAOA_K-Net+Segan) is presented for classification and segmentation of brain tumor utilizing MRI images. Here, pre-processing phase performs noise removal from input image utilizing the Laplacian filter and also the region of interest (ROI) extraction is also carried out. Then, segmentation of brain tumor is conducted by K-Net+Segan, which is combined by Motyka similarity. However, K-Net+Segan for segmentation is trained by GAOA that is an amalgamation of Gannet Optimization Algorithm (GOA) and Aquila Optimizer (AO). From segmented image, features are extracted for performing classification phase. At last, brain tumor classification is conducted by DMN, which is tuned by GAOA and thus, output is obtained. Furthermore, GAOA_K-Net+Segan obtained better outcomes in terms of segmentation accuracy whereas devised GAOA_DMN achieved maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 92.7%, 94.5% and 91.5%.
Provider: - Institution: Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg - Data provided by Europeana Collections- Inschrift: Inschrift — hebräisch- All metadata ...published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg - Data provided by Europeana Collections- Inschrift: Inschrift — hebräisch- All metadata ...published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg - Data provided by Europeana Collections- Inschrift: Inschrift — hebräisch- All metadata ...published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg - Data provided by Europeana Collections- All metadata published by Europeana are available free ...of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
Provider: - Institution: Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg - Data provided by Europeana Collections- Inschrift: Inschrift — hebräisch- Ornament: ...Blattranke- All metadata published by Europeana are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, Europeana requests that you actively acknowledge and give attribution to all metadata sources including Europeana
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