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The enhancement of speech degraded with the non-stationary noise types that typify real-world conditions has remained a challenging problem for several decades. However, recent use of data driven methods for this task has brought great performance improvements. In this paper, we develop a speech enhancement framework based on the extreme learning machine. Experimental results show that the proposed...
AdaBoost is an iterative algorithm to construct classifier ensembles. It quickly achieves high accuracy by focusing on objects that are difficult to classify. Because of this, AdaBoost tends to overfit when subjected to noisy datasets. We observe that this can be partially prevented with the use of validation sets, taken from the same noisy training set. But using less than the full dataset for training...
The localness inference problem is to identify whether a person is a local resident in a city or not and the likelihood of a venue to attract local people. This information is critical for many applications such as targeted ads of local business, urban planning, localized news and travel recommendations. While there are prior work on geo-locating people in a city using supervised learning approaches,...
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