The Experts below are selected from a list of 14910 Experts worldwide ranked by ideXlab platform
Hao Mu - One of the best experts on this subject based on the ideXlab platform.
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A psychoacoustical Preprocessing Technique for virtual bass enhancement of the parametric loudspeaker
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Hao MuAbstract:The parametric loudspeaker is a novel type of loudspeaker that can project a directional sound beam. It is commonly used in creating personal sound zone and projecting private messages to a targeted audience. However, the parametric loudspeaker possesses a very poor bass (or low-frequency) response due inherently to the nonlinear acoustic principle generating sound from ultrasound in air. A psychoacoustic signal processing method known as “virtual bass” has been successfully implemented in some consumer electronics with miniature or flat loudspeaker unit, aiming to enhance their bass performances. In this paper, we adapt this “virtual bass“ approach for parametric loudspeakers. Unlike conventional loudspeakers, the parametric loudspeaker brings in an added degree of complexity in “virtual bass” enhancement due to its inherent nonlinear acoustic property. Accordingly, a new Preprocessing Technique is proposed for the parametric loudspeaker to psychoacoustically reproduce the low-frequency components within an octave below its cut-off frequency.
Nguyen Tran Quoc Vinh - One of the best experts on this subject based on the ideXlab platform.
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effective text data Preprocessing Technique for sentiment analysis in social media data
Knowledge and Systems Engineering, 2019Co-Authors: Saurav Pradha, Malka N Halgamuge, Nguyen Tran Quoc VinhAbstract:In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data Preprocessing Technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different Preprocessing Techniques on the data collected from Twitter using various Techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data Preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming Technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data Preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional Techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data Preprocessing Technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.
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KSE - Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data
2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019Co-Authors: Saurav Pradha, Malka N Halgamuge, Nguyen Tran Quoc VinhAbstract:In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data Preprocessing Technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different Preprocessing Techniques on the data collected from Twitter using various Techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data Preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming Technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data Preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional Techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data Preprocessing Technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.
Lisa Meilhac - One of the best experts on this subject based on the ideXlab platform.
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on spatio frequential smoothing for joint angles and times of arrival estimation of multipaths
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Ahmad Bazzi, Dirk Slock, Lisa MeilhacAbstract:A natural extension of the "Spatial" smoothing Preprocessing Technique is presented and analysed. It is well known that subspace methods do not work properly in the presence of coherent sources. In this paper, a "Spatio-Frequential" smoothing Technique is described when the transmit OFDM symbol is received through multiple coherent signals using a uniform linear antenna array. After this Preprocessing Technique, one could efficiently apply any 2-dimensional subspace method to jointly estimate the angles and times of arrival of the incoming coherent signals. Simulation results demonstrate the potential of the proposed 2D smoothing method over existing separate spatial or frequential smoothing Techniques.
Galina Lavrentyeva - One of the best experts on this subject based on the ideXlab platform.
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automatic Preprocessing Technique for detection of corrupted speech signal fragments for the purpose of speaker recognition
International Conference on Speech and Computer, 2015Co-Authors: Konstantin Simonchik, Sergei Aleinik, Dmitry Ivanko, Galina LavrentyevaAbstract:In this paper we propose a Preprocessing Technique which allows to detect clicks, tones, overloads, clipping, etc., as well as to discover the parts of good-quality speech signal. As a result the performance of the speaker recognition system increases significantly. It should be noted that when describing noise detectors we aim only to provide a full list of algorithms we used as well as their parameters that we obtained in our experiments. The main goal of the paper is to demonstrate that using a set of simple detectors is very effective in detecting speech for speaker recognition task under the conditions of real noise.
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SPECOM - Automatic Preprocessing Technique for Detection of Corrupted Speech Signal Fragments for the Purpose of Speaker Recognition
Speech and Computer, 2015Co-Authors: Konstantin Simonchik, Sergei Aleinik, Dmitry Ivanko, Galina LavrentyevaAbstract:In this paper we propose a Preprocessing Technique which allows to detect clicks, tones, overloads, clipping, etc., as well as to discover the parts of good-quality speech signal. As a result the performance of the speaker recognition system increases significantly. It should be noted that when describing noise detectors we aim only to provide a full list of algorithms we used as well as their parameters that we obtained in our experiments. The main goal of the paper is to demonstrate that using a set of simple detectors is very effective in detecting speech for speaker recognition task under the conditions of real noise.
Saurav Pradha - One of the best experts on this subject based on the ideXlab platform.
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effective text data Preprocessing Technique for sentiment analysis in social media data
Knowledge and Systems Engineering, 2019Co-Authors: Saurav Pradha, Malka N Halgamuge, Nguyen Tran Quoc VinhAbstract:In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data Preprocessing Technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different Preprocessing Techniques on the data collected from Twitter using various Techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data Preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming Technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data Preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional Techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data Preprocessing Technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.
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KSE - Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data
2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019Co-Authors: Saurav Pradha, Malka N Halgamuge, Nguyen Tran Quoc VinhAbstract:In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data Preprocessing Technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different Preprocessing Techniques on the data collected from Twitter using various Techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data Preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming Technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data Preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional Techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data Preprocessing Technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.