Sentence examples for frame classification from inspiring English sources

The proposed procedure consists in five phases: recognition, video recording, subtasks separation, video analysis, and frame classification.

An iterative classification is employed to perform the frame classification into speech or silence classes.

In the proposed analysis framework voiced/unvoiced frame classification is carried out using pitch detector.

In addition, the speech/non-speech frame classification in this scheme is based on the logarithmic magnitude spectra and the logarithmic energy feature of the frames.

One other slight modification is that early stopping is performed by monitoring the cross-entropy cost with soft targets from the teacher on a development set, rather than the frame classification rate or cross-entropy cost with hard aligned targets.

For example, in our previously proposed method, silence feature normalization (SFN) [35], high-pass-filtered log E is used as the indicator for speech/non-speech frame classification, and the log E features of non-speech frames are set to be small, while those of speech frames are kept nearly unchanged.

The segments in this system are delimited by the transition of the frame-by-frame classification and no smoothing is applied.

Based on this verification, frame image classification is performed.

Each category has been qualitatively investigated focusing on frame format, classification of a patient's data, and MA scheduling schemes.

Thus, a method based on features that would allow coding open reading frame (ORF) classification independently of the codon usage is desired.

In this study, we investigated the modalities of coding open reading frame (cORF) classification of expressed sequence tags (EST) by using the universal feature method (UFM).