Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.

On Cooley-Tukey FFT method for zero padded signals

Abstract: The classical Cooley-Tukey fast Fourier transform (FFT) algorithm has the computational cost of O(Nlog2N) where N is the length of the discrete signal. Spectrum resolution is improved through padding zeros at the tail of the discrete signal, if (p -1)N zeros are padded (where p is an integer) at the tail of the data sequence, the computational cost through FFT becomes O(pNlog2pN). This paper proposes an alternate instance of padding zeros to the data sequence that results in computational cost reduction to O(pNlog2 N). It has been noted that this modification can be used to achieve non-uniform upsampling that would zoom-in or zoom-out a particular frequency band, in addition, it may be used for pruning the spectrum, which would reduce resolution of an unimportant frequency band

R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method

Abstract: Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines a local reference frame (LRF) for every point using its nearest neighbors and finds local attributes. Next, R-PointHop obtains local-to-global hierarchical features by point downsampling, neighborhood expansion, attribute construction and dimensionality reduction steps. Thus, point correspondences are built in hierarchical feature space using the nearest neighbor rule. Afterwards, a subset of salient points with good correspondence is selected to estimate the 3D transformation. The use of the LRF allows for invariance of the hierarchical features of points with respect to rotation and translation, thus making R-PointHop more robust at building point correspondence, even when the rotation angles are large. Experiments are conducted on the 3DMatch, ModelNet40, and Stanford Bunny datasets, which demonstrate the effectiveness of R-PointHop for 3D point cloud registration. R-PointHop's model size and training time are an order of magnitude smaller than those of deep learning methods, and its registration errors are smaller, making it a green and accurate solution. Our codes are available on GitHub.

Speech processing method and speech processing device based on artificial intelligence

Abstract: The invention provides a speech processing device and a speech processing device based on artificial intelligence. The speech processing method comprises steps that a speech processing request is received, and comprises a first digital speech signal and a first sampling frequency corresponding to the first digital speech signal; according to the first sampling frequency, a target speech processing model is selected from a pre-trained speech processing model base; the target speech processing model is used for the upsampling processing of the first digital speech signal, and is used for generating a second digital speech signal having a second sampling frequency, and in addition, the second sampling frequency is greater than the first sampling frequency. The speech processing device and the speech processing device based on the artificial intelligence are advantageous in that the upsampling of the digital speech signal having the low sampling frequency is realized, and the timbre of the speech signal is improved, and user experience is improved.

Upsampling filter having one-bit multipliers for multiple spread-data streams

Abstract: A transmit portion of a WB-CDMA transceiver generates one or more spread data streams having values represented by a single bit, allowing for filtering of spread and combined data streams with a root raised cosine (RRC) filter employing single-bit multipliers. The RRC filter is a digital filter that i) employs multiplication of two values in which the length of at least one value is one bit; ii) is preferably implemented with muxs or a simple logic operator; and iii) may employ upsampling and modulation encoding of filter coefficients to reduce the coefficient length to, for example, one bit. The RRC filter may be an FIR filter having either one-bit or multi-bit coefficients, and apply RRC filtering to a spread user stream either before or after the spread user streams are combined. For some implementations, RRC filters are employed to filter each spread user stream prior to combining several processed user steams. For other implementations, the multi-bit valued data stream representing the combined user streams is upsampled to form an upsampled data stream of single-bit values, and RRC filtering is then applied to the upsampled data stream. Alternatively, implementations may use upsampled RRC filter coefficients that allow RRC filtering on the combined spread user streams represented as a sequence of multi-bit values.

Directionlets Using In-Phase Lifting for Image Representation

Abstract: Directionlets allow a construction of perfect reconstruction and critically sampled multidirectional anisotropic basis, yet retaining the separable filtering of standard wavelet transform. However, due to the spatially varying filtering and downsampling direction, it is forced to apply spatial segmentation and process each segment independently. Because of this independent processing of the image segments, directionlets suffer from the following two major limitations when applied to, say, image coding. First, failure to exploit the correlation across block boundaries degrades the coding performance and also induces blocking artifacts, thus making it mandatory to use de-blocking filter at low bit rates. Second, spatial scalability, i.e., minimum segment size or the number of levels of the transform, is limited due to independent processing of segments. We show that, with simple modifications in the block boundaries, we can overcome these limitations by, what we call, in-phase lifting implementation of directionlets. In the context of directionlets using in-phase lifting, we identify different possible groups of downsampling matrices that would allow the construction of a multilevel transform without forcing independent processing of segments both with and without any modifications in the segment boundary. Experimental results in image coding show objective and subjective improvements when compared with the directionlets applied independently on each image segment. As an application, using both the in-phase lifting implementation of directionlets and the adaptive directional lifting, we have constructed an adaptive directional wavelet transform, which has shown improved image coding performance over these adaptive directional wavelet transforms.