scholarly journals Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure

2017 ◽  
Vol 10 (1) ◽  
pp. 114-119
Author(s):  
K Geetha ◽  
R Vadivel
Keyword(s):  
Native Speakers ◽  
Research Work ◽  
Information Criterion ◽  
Spoken Word ◽  
Phoneme Segmentation ◽  
Short Term ◽  
Spectral Transition ◽  
Term Energy ◽  
Transition Measure ◽  
Recognition Systems

Process of identifying the end points of the acoustic units of the speech signal is called speech segmentation. Speech recognition systems can be designed using sub-word unit like phoneme. A Phoneme is the smallest unit of the language. It is context dependent and tedious to find the boundary. Automated phoneme segmentation is carried in researches using Short term Energy, Convex hull, Formant, Spectral Transition Measure(STM), Group Delay Functions, Bayesian Information Criterion, etc. In this research work, STM is used to find the phoneme boundary of Tamil speech utterances. Tamil spoken word dataset was prepared with 30 words uttered by 4 native speakers with a high quality microphone. The performance of the segmentation is analysed and results are presented.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

Lack of Effect of Exercise Time of Day on Acute Energy Intake in Healthy Men

2010 ◽  
Vol 20 (4) ◽  
pp. 350-356 ◽  
Author(s):  
Katriona J.M O’Donoghue ◽  
Paul A. Fournier ◽  
Kym J. Guelfi
Keyword(s):  
Energy Intake ◽  
Time Of Day ◽  
Treadmill Running ◽  
Short Term ◽  
Exercise Time ◽  
Acute Bout ◽  
Healthy Men ◽  
Significant Difference ◽  
Term Energy ◽  
Successful Weight Loss

Although the manipulation of exercise and dietary intake to achieve successful weight loss has been extensively studied, it is unclear how the time of day that exercise is performed may affect subsequent energy intake. The purpose of the current study was to investigate the effect of an acute bout of exercise performed in the morning compared with an equivalent bout of exercise performed in the afternoon on short-term energy intake. Nine healthy male participants completed 3 trials: morning exercise (AM), afternoon exercise (PM), or control (no exercise; CON) in a randomized counterbalanced design. Exercise consisted of 45 min of treadmill running at 75% VO2peak. Energy intake was assessed over a 26-hr period with the participants eating ad libitum from a standard assortment of food items of known quantity and composition. There was no significant difference in overall energy intake (M ± SD; CON 23,505 ± 6,938 kJ, AM 24,957 ± 5,607 kJ, PM 24,560 ± 5,988 kJ; p = .590) or macronutrient preferences during the 26-hr period examined between trials. Likewise, no differences in energy intake or macronutrient preferences were observed at any of the specific individual meal periods examined (i.e., breakfast, lunch, dinner) between trials. These results suggest that the time of day that exercise is performed does not significantly affect short-term energy intake in healthy men.

scholarly journals Short-Term Energy Balancing With Increasing Levels of Wind Energy

2012 ◽  
Vol 3 (4) ◽  
pp. 769-776 ◽  
Author(s):  
Juha Kiviluoma ◽  
Peter Meibom ◽  
Aidan Tuohy ◽  
Niamh Troy ◽  
Michael Milligan ◽  
...  
Keyword(s):  
Wind Energy ◽  
Energy Balancing ◽  
Short Term ◽  
Term Energy
Sign in / Sign up

Export Citation Format

Share Document