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The S-Transform which is used in many fields, is better in the methods of time-frequency analysis. Although the S-Transform improves the time-frequency analysis, it also increases the algorithm complexity, resulting in rapidly increasing the computing time and needing more memory. Therefore, the S-Transform is difficult to be applied for real-time procession of signal. Based on the characteristics that human being is insensitive to the voice frequency resolution, in this paper, we propose a compressing algorithm of the S-transform. Through re-sampling frequency, the algorithm decreases data size, the computing time and computer memory usage in the S-transform. The application of the algorithm in MFCC analysis shows the results is reliable under the condition of re-sampling frequency resolution .

The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work. Existing solutions to this problems largely rely on hand-craft features and vanishing lines, and they often fail in highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: 1) coarse layout estimation; and 2) fine layout localization. In the first stage, we adopt a fully convolutional neural network (FCN) to obtain a coarse-scale room layout estimate that is close to the ground truth globally. The proposed FCN considers combines the layout contour property and the surface property so as to provide a robust estimate in the presence of cluttered objects. In the second stage, we formulate an optimization framework that enforces several constraints such as layout contour straightness, surface smoothness and geometric constraints for layout detail refinement. Our proposed system offers the state-of-the-art performance on two commonly used benchmark datasets.