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Pitch is an important characteristic of speech and is useful for many applications. However, it is still challenging to estimate pitch in strong noise. In this paper, we propose a joint training approach to determinate pitch. First, a Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTMRNN) is trained to map the noisy to clean speech features. Second, the pitch estimation is also...
Accurate pitch extraction from speech is important but challenging problem for speech synthesis. However, the additive nature and long-term suprasegmental property of pitch features have not been fully exploited in most of the existing pitch estimators as they are operated frame by frame. As a result, they would cause some inherent discontinuities, such as double/half F0 errors and unvoiced/voiced...
In this paper, we present a number-of-classes-adaptive unsupervised classification framework for synthetic aperture radar (SAR) images. The framework aims at the provision of robust classification for SAR images even if the number of classes existing in the scene is unknown. It mainly consists of estimation of the number of classes, extraction of each class center, classification of image patches,...
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