An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future

Nanoparticles of a pyrazolo-pyridazine derivative as potential EGFR and CDK-2 inhibitors: design, structure determination, anticancer evaluation and in silico studies

The strategic planning of this study is based upon using the nanoformulation method to prepare nanoparticles 4-SLNs and 4-LPHNPs of the previously prepared 4,5-diphenyl-1H-pyrazolo[3,4-c]pyridazin-3-amine (4) after confirming its structure with single crystal X-ray analysis. These nanoparticles exhibited promising cytotoxic activity against HepG-2, HCT-116 and MCF-7 cancer cell lines in comparison with the reference doxorubicin and the original derivative 4. Moreover, their inhibitory assessment against EGFR and CDK-2/cyclin A2 displayed improved and more favorable impact than the parent 4 and the references. Detection of their influence upon cancer biomarkers revealed upregulation of Bax, p53 and caspase-3 levels and downregulation of Bcl-2 levels. The docking simulation demonstrated that the presence of the pyrazolo[3,4-c]pyridazin-3-amine scaffold is amenable to enclosure and binding well within EGFR and CDK-2 receptors through different hydrophilic interactions. The pharmacokinetic and physicochemical properties of target 4 were also assessed with ADME investigation, and the outcome indicated good drug-like characteristics

Tissue hyaluronan expression, as reflected in the sputum of lung cancer patients, is an indicator of malignancy

Hyaluronan (HA) shows promise for detecting cancerous change in pleural effusion and urine. However, there is uncertainty about the localization of HA in tumor tissue and its relationship with different histological types and other components of the extracellular matrix, such as angiogenesis. We evaluated the association between HA and degree of malignancy through expression in lung tumor tissue and sputum. Tumoral tissue had significantly increased HA compared to normal tissue. Strong HA staining intensity associated with cancer cells was significant in squamous cell carcinoma compared to adenocarcinoma and large cell carcinoma. A significant direct association was found between tumors with a high percentage of HA and MVD (microvessel density) in tumoral stroma. Similarly significant was the direct association between N1 tumors and high levels of HA in cancer cells. Cox multivariate analysis showed significant association between better survival and low HA. HA increased in sputum from lung cancer patients compared to cancer-free and healthy volunteers and a significant correlation was found between HA in sputum and HA in cancer tissue. Localization of HA in tumor tissue was related to malignancy and reflected in sputum, making this an emerging factor for an important diagnostic procedure in patients suspected to have lung cancer. Further study in additional patients in a randomized prospective trial is required to finalize these results and to validate our quantitative assessment of HA, as well as to couple it to gold standard sputum cytology.Research supported by FAPESP (2010/11005-5 and 2010/04462) and CNPq (#471939/2010-2 and 483005/2012-6

A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids

With the fast progression of the speech enhancement field after the introduction of deep learning techniques, there is a need to consider the adjustments needed to employ these techniques for real-life applications. In this work, we present an optimised deep learning speech enhancement architecture for automatic speech recognition and hearing aids, two key speech enhancement applications. A speech enhancement architecture with a signal-to-noise ratio switch is presented for automatic speech recognition systems, to avoid denoising artifacts that cause performance degradation in the case of clean or high signal-tonoise speech. Moreover, a smart speech enhancement architecture is presented for hearing aids to retain important emergency noise in the audio signal. The presented work achieved 13.9% reduction in the word error rate of an automatic speech recognition system. Additionally, the smart speech enhancement architecture resulted in 0.18 improvement in HAAQI audio quality metric

Enhancing Automatic Speech Recognition Quality with a Second-Stage Speech Enhancement Generative Adversarial Network

Speech enhancement is an essential preprocessing stage for automatic speech recognition in noisy conditions; however, the distortion caused by the denoising process may lead to degradation in automatic speech recognition performance. This paper presents a deep learning-based speech enhancement architecture to overcome this issue by applying a second stage network that deals with distortion noise. Moreover, a signal-to-noise ratio binary classifier is implemented to activate the speech enhancement network for intrusive noise environments only, which improves the overall performance. The proposed architecture outperforms powerful models in the literature, as it improves a challenging noisy speech test set by 0.8 and 5.9% improvement in the quality and intelligibility scores, respectively. Furthermore, the architecture improves the performance of automatic speech recognition with a 13.8% reduction in the word error rate at 0 dB signal-to-noise ratio. Finally, the second-stage network was proven to improve the performance of first-stage speech enhancement models, not previously seen in the training process

Novel synthesis of pyrazole-containing thiophene, 2-alkyloxy-pyridine and thieno[2,3-d]pyrimidine scaffolds as analgesic agents

ABSTRACT. A group of trisubstituted pyrazoles containing thiophen, 2-alkyloxypyridine and thieno[2,3-d]pyrimidine heterocycles were synthesized in a study for possible analgesic agents. The desired products were obtained by reaction of 2-((1-(3-chlorophenyl)-3-(4-methoxyphenyl)-1H-pyrazol-4-yl)methylene)malononitrile with sulfur in presence of TEA, followed by treatment with different reagents. Newer products were examined for their analgesic properties, among them, analog 7 showed significant analgesic effects in comparison with reference medicines activity. KEY WORDS: Trisubstituted pyrazoles, Thiophene, Alkyloxypyridine, Fused pyrimidine, Analgesic activities Bull. Chem. Soc. Ethiop. 2019, 33(3), 505-515. DOI: https://dx.doi.org/10.4314/bcse.v33i3.1

New Quinazolin-4(3<i>H</i>)-one Derivatives Incorporating Hydrazone and Pyrazole Scaffolds as Antimicrobial Agents Targeting DNA Gyraze Enzyme

The present work includes the synthesis of a new series of quinazolin-4(3H)-one compounds (4a–f, 5a–d) as antimicrobial agents. The starting compound, 2-hydrazinylquinazolin-4(3H)-one (2), was synthesized and treated with different carbonyl compounds to afford the hydrazone derivatives 4a–f. In addition, the hydrazone derivatives 4a–d were treated with a DMF/POCl3 mixture to give the formyl-pyrazole derivatives 5a–d. All the target compounds were evaluated as antimicrobial agents against four bacterial and four fungal strains. The majority of the tested compounds showed potent antimicrobial activity compared with the reference antibiotics. The most potent antimicrobial activity was shown by 5a with MIC values in the range (1–16) μg/mL. In addition, the most potent compounds against E. coli were evaluated for their inhibitory activity against E. coli DNA gyrase, whereas the target compounds 4a, 5a, 5c, and 5d showed the most potent inhibition to the target enzyme with IC50 values ranging from 3.19 to 4.17 µM. Furthermore, molecular docking studies were performed for the most active compounds against the target E. coli DNA gyrase to determine their binding affinity within the enzyme’s active site. Moreover, ADME evaluations of these compounds predicted their high oral bioavailability and good GI absorption

New Quinazolin-4(3H)-one Derivatives Incorporating Hydrazone and Pyrazole Scaffolds as Antimicrobial Agents Targeting DNA Gyraze Enzyme

The present work includes the synthesis of a new series of quinazolin-4(3H)-one compounds (4a&ndash;f, 5a&ndash;d) as antimicrobial agents. The starting compound, 2-hydrazinylquinazolin-4(3H)-one (2), was synthesized and treated with different carbonyl compounds to afford the hydrazone derivatives 4a&ndash;f. In addition, the hydrazone derivatives 4a&ndash;d were treated with a DMF/POCl3 mixture to give the formyl-pyrazole derivatives 5a&ndash;d. All the target compounds were evaluated as antimicrobial agents against four bacterial and four fungal strains. The majority of the tested compounds showed potent antimicrobial activity compared with the reference antibiotics. The most potent antimicrobial activity was shown by 5a with MIC values in the range (1&ndash;16) &mu;g/mL. In addition, the most potent compounds against E. coli were evaluated for their inhibitory activity against E. coli DNA gyrase, whereas the target compounds 4a, 5a, 5c, and 5d showed the most potent inhibition to the target enzyme with IC50 values ranging from 3.19 to 4.17 &micro;M. Furthermore, molecular docking studies were performed for the most active compounds against the target E. coli DNA gyrase to determine their binding affinity within the enzyme&rsquo;s active site. Moreover, ADME evaluations of these compounds predicted their high oral bioavailability and good GI absorption

Anticancer evaluation and molecular modeling of multi-targeted kinase inhibitors based pyrido[2,3-d]pyrimidine scaffold

An efficient synthesis of substituted pyrido[2,3-d]pyrimidines was carried out and evaluated for in vitro anticancer activity against five cancer cell lines, namely hepatic cancer (HepG-2), prostate cancer (PC-3), colon cancer (HCT-116), breast cancer (MCF-7), and lung cancer (A-549) cell lines. Regarding HepG-2, PC-3, HCT-116 cancer cell lines, 7-(4-chlorophenyl)-2-(3-methyl-5-oxo-2,3-dihydro-1H-pyrazol-1-yl)-5-(p-tolyl)- pyrido[2,3-d]pyrimidin-4(3H)-one (5a) exhibited strong, more potent anticancer (IC50: 0.3, 6.6 and 7 µM) relative to the standard doxorubicin (IC50: 0.6, 6.8 and 12.8 µM), respectively. Kinase inhibitory assessment of 5a showed promising inhibitory activity against three kinases namely PDGFR β, EGFR, and CDK4/cyclin D1 at two concentrations 50 and 100 µM in single measurements. Further, a molecular docking study for compound 5a was performed to verify the binding mode towards the EGFR and CDK4/cyclin D1 kinases