Pravara Rural Engineering College

About: Pravara Rural Engineering College is a based out in . It is known for research contribution in the topics: PID controller & Control theory. The organization has 225 authors who have published 120 publications receiving 418 citations.

DWT and LPC based feature extraction methods for isolated word recognition

Abstract: In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide better representation than modeling the frame directly. The WLPC coefficients have been further normalized in cepstrum domain to get new set of features denoted as wavelet subband cepstral mean normalized features. The proposed approaches provide effective (better recognition rate), efficient (reduced feature vector dimension), and noise robust features. The performance of these techniques have been evaluated on the TI-46 isolated word database and own created Marathi digits database in a white noise environment using the continuous density hidden Markov model. The experimental results also show the superiority of the proposed techniques over the conventional methods like linear predictive cepstral coefficients, Mel-frequency cepstral coefficients, spectral subtraction, and cepstral mean normalization in presence of additive white Gaussian noise.

Time series decomposition and predictive analytics using MapReduce framework

Abstract: The recent development in precision agriculture, a large amount of data are generated by site-specific weather stations which will demand a platform for the processing and predictive weather analytics. The sophisticated methodology to solve large amount of data handling problem and process data in a small time is important. In this study, future conditions are predicted from weather stations large data by proposing the predictive approaches based on time series and neural network using MapReduce programming model. We have proposed predictive analytics approaches including the modules, i.e., analysis and decomposition, classification, and prediction. The time series based decomposition approach is proposed to decompose and find out the trend, regular and sophisticated components. The linear components are handled by time series MapReduce based Autoregressive Integrated Moving Average (M-ARIMA) model and nonlinear components are handled by M-K-Nearest Neighbors (M-KNN) model. In addition, the MapReduce-based Hybrid Model (M-HM) was proposed which will use the advantages of time series and neural network to increase prediction accuracy. The study verifies the effectiveness of proposed model over the regular and randomness component of the data. The performance measures and statistical test are performed to validate and check data consistency. In addition, excellent speed-up, scale-up, and size-up were tested by changing the size of data set. However, when the data size increases, the average execution time is reduced by using the MapReduce-based approach over the multiple-node workers.

Voice activity detection Algorithm for Speech Recognition Applications

Abstract: Determining the beginning and the termination of speech in the presence of background noise is a complicated problem. This paper is concerned with labeling sections of speech samples based on whether they are silence, voiced or unvoiced speech. The labeling is done using calculations over the speech samples; zero crossing and short-term energy functions. The short-term energy and zero crossing rate of speech have been extensively used to detect the endpoints of an utterance. General Terms Speech Recognition, Voice, Unvoice.

Studies on auramine dye adsorption on psidium guava leaves

Abstract: Removal of auramine dye from aqueous waste solutions was investigated by using very cheap and bio- sorbent, withered guava tree leaves and activated carbon. Guava leaves are readily available in the western and northern parts of India throughout the year, and hence form a cost effective alternative for removal of dyes from waste waters. The optimum contact time was found to be 120 min. in a pH range of 8-9 for 92-94% removal of the dye from aqueous solutions containing 150 mg/L of auramine dye using 2 g of the adsorbent. The effect of pH, dye concentration, sorbent dosage, temperature and contact time on the dye removal efficiency has been studied. Experimental results were found to fit both Freundlich and Langmuir models. Since the dye contains a cationic species, the removal efficiency was highest in a pH range of 8-9. Continuous adsorption studies in a packed column showed 100% removal efficiency for a flow rate of 10 ml·min −1 . When compared with the activated carbon, it was also found that adsorbent derived from guava leaves is more efficient in removal of dye.