Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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Abstract: The author describes a voltammetric sensor for the simultaneous determination of formaldehyde and ammonia using rGO/ZnO composite micro-dumbbell shaped rods. A facile hydrothermal technique was adopted to synthesize rGO/ZnO composite based micro-dumbbell shaped rods. The potential application based on obtained product has been employed as electrochemical sensor towards the detection of ammonia and formaldehyde using hexamine. For performing an electrochemical sensing application, glassy carbon electrode was modified with rGO/ZnO composite rods and analysed for the formation of multiple peaks thus emphasizing the detection of formaldehyde and ammonia using hexamine. Hexamine is toxic food preservative when it dissolutes in saline medium containing pH 7.4 emits ammonia and formaldehyde. The crystallinity property has been examined using X-ray diffraction technique revealing composite rods (rGO/ZnO) with 29.5 nm were lesser than ZnO NPs (38.33 nm). The surface morphology was investigated by scanning electron microscopy (SEM). In order to examine limit of detection (LOD) and sensitivity, cyclic voltammetry was carried out to determine the formaldehyde and ammonia. The voltammetric results revealed three different redox peaks with increased hexamine concentrations in which 0.4 V for formaldehyde, 0.13 V for ammonia and ~ 0.3 V for Zn2+ ions. The calibrated curve for ammonia with LOD and LOQ was found to be 0.1 µM and 0.9 µM and for formaldehyde with 0.023 µM and 0.07 µM respectively. Based on investigated results, the modified electrode was applied to the determination of ammonia and formaldehyde in urine samples (real-time analysis) and the results proved that the method is sensitive and can be an alternative to chromatographic and spectroscopic techniques. Graphic Abstract: [Figure not available: see fulltext.] © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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School of Electronics Engineering

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Journal of Inorganic and Organometallic Polymers and Materials

Synthesis of Micro-dumbbell Shaped rGO/ZnO Composite Rods and Its Application Towards as Electrochemical Sensor for the Simultaneous Determination of Ammonia and Formaldehyde Using Hexamine and Its Structural Analysis

This research work proposes a new Genetic Algorithm (GA) based fusion scheme to effectively fuse the Time–Frequency (T–F) binary mask of voiced and unvoiced speech. The perceptual cues such as correlogram, cross-correlogram and pitch are commonly used to obtain the T–F binary mask of voiced speech. Recently, researchers use speech onset and offset to segment the unvoiced speech from the noisy speech mixture. Most of the research work which uses speech onset and offset to represent the unvoiced speech, combine the segments of unvoiced speech with the segments of voiced speech to obtain the T–F binary mask. This research work effectively fuses the T–F binary mask of voiced and unvoiced speech, instead of combining the segments of voiced and unvoiced speech using a Genetic Algorithm (GA). Moreover, a new method is proposed in this research work to obtain a T–F binary mask from the segments of unvoiced speech. The performance of the proposed GA based fusion scheme is evaluated using measures such as quality and intelligibility. The experimental results show that the proposed system enhances the speech quality by increasing the SNR with an average value of 10.74 dB and decreases the noise residue with an average value of 26.15% when compared with noisy speech mixture and enhances the speech intelligibility by increasing the CSII, NCM and STOI with an average value of 0.22, 0.20 and 0.17 as compared with the conventional speech segregation systems. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

A new Genetic Algorithm based fusion scheme in monaural CASA system to improve the performance of the speech

In this research work, we propose the model based on the Genetic Algorithm (GA) and Deep Neural Network (DNN) to enhance the quality and intelligibility of the noisy speech. In this proposed model, the Voiced Speech (VS) T-F mask is computed using correlogram, frame energy and cross-channel correlogram and Unvoiced Speech (UVS) T-F mask is computed using speech onset/offset. The T-F mask obtained using speech onset and offset represents both voiced and unvoiced segment of the noisy speech signal. The UVS T-F mask is obtained by subtracting the VS from the T-F mask obtained earlier using speech onset/offset. Next, the GA is used to find the optimum weight to combine the T-F mask of VS and UVS to improve speech quality and intelligibility. The weight obtained using GA may not be an optimum one for all sets of speech and noise. This research work focuses on this issue and proposes a DNN model to estimate the optimum weight for all sets of speech and noise. The DNN model is trained using features and optimum weight obtained using GA. Later, the trained DNN model is used to estimate the optimum weight for the testing speech and noise samples. The performance of the proposed GA-DNN based model is evaluated using objective and subjective quality and intelligibility measures. The results of the proposed model shows a prompt improvement in the speech quality and intelligibility with average of 0.73, 4.07, 0.17, 0.26 and 0.22 for PESQ, SNR, STOI, CSII and NCM when compared with the existing speech separation systems. © 2019 Elsevier Ltd

Applied Acoustics

Monaural speech separation using GA-DNN integration scheme

Improving the speech intelligibility remains a challenging problem in digital hearing aids. This research work proposes a new speech segregation algorithm to improve the speech intelligibility by effectively fusing the voiced and unvoiced segment of the speech signal using the genetic algorithm. The voiced speech segments are obtained using perceptual speech cues such as auto-correlation, cross-channel correlation and pitch. Similarly, the unvoiced speech segments are obtained using another perceptual speech cue onset/offset after subtracting the voiced segments. The speech onset- and offset-based segregation process actually produce segments for both voiced and unvoiced. The unvoiced speech segments are obtained by subtracting the voiced speech segments from the segments obtained using speech onset and offset. The unvoiced speech segments obtained using onset and offset may contain interference. This research work proposes a scheme to remove those interferences from the unvoiced speech segments and effectively fuse the segments of voiced and unvoiced speech using the genetic algorithm. The performance of the proposed algorithm is evaluated using the intelligibility measures such as CSII, NCM and STOI. The experimental results show that the proposed algorithm significantly improves the speech intelligibility with an average of 0.23 for CSII, 0.20 for NCM and 0.16 for STOI as compared with other existing systems. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Circuits, Systems, and Signal Processing

Improving Speech Intelligibility in Monaural Segregation System by Fusing Voiced and Unvoiced Speech Segments

Speech Intelligibility is of prime importance in places of mass gathering such as railway stations and airports as it is imperative for the speech to be understandable for the benefit and safety of the people. Often, the desired speech is corrupted by external noise which leads to discrepancies in the way it is perceived by the target audience. Therefore, the purpose of a speech enhancement algorithm is to identify and eliminate the noise. Recent advancements in speech processing and machine learning have resulted in various methods to do this. This research aims to improve speech intelligibility using Deep Neural Network (DNN) based speech enhancement system. Optimum Soft Mask (OSM) is used as a training target for the DNN system. The performance of the system is evaluated using Short Time Objective Intelligibility (STOI) metric. It is found that the proposed system provided good enhancement of noisy speech without compromising on intelligibility. In addition, it is found that usage of optimum soft mask instead of an ideal binary mask (IBM) provided additional improvement in speech intelligibility. © 2019 IEEE.

2019 4th IEEE International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2019 - Proceedings

Improving Speech Intelligibility using Deep Learning

Speech enhancement is one of the speech processing techniques which is perturbed mostly with increasing some perceptual part of speech like quality and intelligibility that has been debased by noise. The intelligibility of a speech can be improved by the isolation of target speech from noisy speech. This work aims to enhance the performance of the speech at the phoneme level. A phoneme is the basic unit of sound. There are 59 phonemes as per the TIMIT speech database. Features such as MFCC, RASTAPLP, AMS, GTFB are extracted for each phoneme at the frame level. The extracted features are given to the classifier for training as a result, a trained model is obtained. Then, Binary Mask based on the frame energy is estimated for each phoneme. An Ideal Binary Mask (IBM) is generated, if the energy of speech is greater than noise then it is assigned 1, otherwise, it is 0. During testing, the trained model gives the label for the given phoneme and the corresponding ideal binary mask is taken for resynthesis. Thus, enhanced speech is obtained. Performance such quality measure (SNR), intelligibility measure (STOI CSII) were calculated for the resultant speech signal. © 2019 IEEE.

Speech enhancement using phoneme level classification

Fusion plays an important role to retrieve the single output data from the input data set and in turn improves the quality of the results. Single channel based separation of speech process is to separate the utter speech from the composite speech recorded with a single mic. This research work proposed a weight fusion scheme to combine the voiced matrix and unvoiced speech matrix obtained using feature based single channel speech separation systems. The voiced segment matrix obtained using periodicity features and unvoiced segment matrix obtained using onset/offset feature are combined using the proposed weight fusion principle. The proposed system is evaluated using the standard speech and noise database. The experimental outcome results of the weight fusion system reveal that there is a better improvement than the other speech separation systems. © 2019 IEEE.

Proceedings of 1st International Conference on Innovations in Information and Communication Technology, ICIICT 2019

Weight Fusion Scheme for single channel based CASA system

Speech separation is a process of segregating the target speech from the noisy mixture. Human auditory system naturally has the capability to separate the speech from background noise; however the machine implementation of the same has been failed. Computational Auditory Scene Analysis (CASA) is a recent approach which models the human auditory system by computational means to separate the target speech from the noisy mixture. Most of the speech separation system based on CASA uses energy as one of the reliable feature to segregate the target speech from the noisy mixture. This research work proposes a new approach using adaptive energy selection threshold to segregate the target speech. The experimental result shows significant improvement in the signal to noise ratio as compared to the conventional energy threshold method. © 2017 IEEE.

Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017

Adaptive energy threshold for monaural speech separation

Monaural speech separation is the process of separating the target speech from the noisy speech mixture recorded using single microphone. It is a challenging problem in speech signal processing, and recently, computational auditory scene analysis (CASA) finds a reasonable solution to solve this problem. This research work proposes an image analysis-based algorithm to enhance the binary T–F mask obtained in the initial segmentation stage of CASA-based monaural speech separation systems to improve the speech quality. The proposed algorithm consists of labeling the initial segmentation mask, boundary extraction, active pixel detection and finally eliminating the noisy non-active pixels. In labeling, the T–F mask obtained from the initial segmentation is labeled as periodicity pixel matrix and non-periodicity pixel matrix. Next boundaries are created by connecting all the possible nearby periodicity pixel matrix and non-periodicity pixel matrix as speech boundary. Some speech boundary may include noisy T–F units as holes, and these holes are treated using the proposed algorithm to properly classify them as the speech-dominant or noise-dominant T–F units in the active pixel detection process. Finally, the noisy T–F units are eliminated. The performance of the proposed algorithm is evaluated using TIMIT speech database. The experimental results show that the proposed algorithm improves the quality of the separated speech by increasing the signal-to-noise ratio by an average value of 9.64 dB and reduces the noise residue by 25.55% as compared to the noisy speech mixture. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.

Image Processing Techniques for Segments Grouping in Monaural Speech Separation

This research work proposes an image analysis-based algorithm to enhance the time-frequency (T-F) mask obtained in the initial segmentation of CASA-based monaural speech separation system to improve speech quality and intelligibility. It consists of labelling the initial segmentation mask, boundary extraction, active pixel detection and eliminating the non-active pixels related to noise. In labelling, the T-F mask obtained is labelled as periodicity pixel (P) matrix and nonperiodicity pixel (NP) matrix. Next speech boundaries are created by connecting all the possible nearby P and NP matrix. Some speech boundary may include noisy T-F units as holes; these holes are treated using the proposed algorithm. The proposed algorithm is evaluated with the quality and intelligibility measures such as signal to noise ratio (SNR), perceptual evaluation of speech quality, PEL, PNR, coherence speech intelligibility index (CSII), normalised covariance metric (NCM), and short-time objective intelligibility (STOI). The experimental results show that the proposed algorithm improves the speech quality by increasing the SNR with an average value of 9.91 dB and reduces the PNR by an average value of 25.6% and also improves the speech intelligibility in terms of CSII, NCM, and STOI when compared with the input noisy speech mixture. © The Institution of Engineering and Technology 2018.

Fulltext

IET Signal Processing

Performance improvement of monaural speech separation system using image analysis techniques

The transform based methods are more popular in pixel level image fusion technique. Image fusion technique is the process to obtain one image from multiple images of the same scene collected from multiple channels or at different times with certain fusion rules. The purpose of image fusion technique is to provide information integrated from different images, to remove redundancy and contradiction existed between the images, to form a clear and accurate description of the observed target, to enhance the transparency of the image information, and to improve the interpretation, accuracy, reliability and utilization of the information. The processing system first decompose the input images according to discrete wavelet transform and discrete cosine transform and then use the appropriate fusion rule to integrate the coefficients of decomposed sub bands. The inverse transform is computed to obtain the fused image of decomposed sub bands. A transform based image fusion technique is implemented in order to improve efficiency in an effective manner. By this method better PSNR (peak signal to noise ratio) value and less MSE (mean square error) can be obtained. © Research India Publications.