Codebook

About: Codebook is a research topic. Over the lifetime, 8492 publications have been published within this topic receiving 115995 citations.

MIMO transmit beamforming under uniform elemental power constraint

Abstract: We consider multi-input multi-output (MIMO) transmit beamforming under the uniform elemental power constraint. This is a non-convex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multi-input single-output (MISO) case, the optimal solution has a closed-form expression. Then we propose a cyclic algorithm for the MIMO case which uses the closed- form MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finite-rate feedback methods needed for transmit beamforming. We propose a novel vector quantization method, where the codebook is constructed under the uniform elemental power constraint and the method is referred as VQ-UEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finite-rate feedback technique.

Gesture recognition by learning local motion signatures

Abstract: This paper overviews a new gesture recognition framework based on learning local motion signatures (LMSs) introduced by [5]. After the generation of these LMSs computed on one individual by tracking Histograms of Oriented Gradient (HOG) [2] descriptor, we learn a codebook of video-words (i.e. clusters of LMSs) using k-means algorithm on a learning gesture video database. Then the video-words are compacted to a codebook of code-words by the Maximization of Mutual Information (MMI) algorithm. At the final step, we compare the LMSs generated for a new gesture w.r.t. the learned codebook via the k-nearest neighbors (k-NN) algorithm and a novel voting strategy. Our main contribution is the handling of the N to N mapping between code-words and gesture labels with the proposed voting strategy. Experiments have been carried out on two public gesture databases: KTH [16] and IXMAS [19]. Results show that the proposed method outperforms recent state-of-the-art methods.

VQ codebook design using genetic algorithms

Abstract: A codebook design approach for vector quantisation using genetic algorithms is proposed This novel approach provides superior performance compared with the generalised Lloyd algorithm (GLA)

On the use of neural networks in articulatory speech synthesis

Abstract: A long‐standing problem in the analysis and synthesis of speech by articulatory description is the estimation of the vocal tract shape parameters from natural input speech. Methods to relate spectral parameters to articulatory positions are feasible if a sufficiently large amount of data is available. This, however, results in a high computational load and large memory requirements. Further, one needs to accommodate ambiguities in this mapping due to the nonuniqueness problem (i.e., several vocal tract shapes can result in identical spectral envelopes). This paper describes the use of artificial neural networks for acoustic to articulatory parameter mapping. Experimental results show that a single feed‐forward neural net is unable to perform this mapping sufficiently well when trained on a large data set. An alternative procedure is proposed, based on an assembly of neural networks. Each network is designated to a specific region in the articulatory space, and performs a mapping from cepstral values into tract areas. The training of this assembly is executed in two stages: In the first stage, a codebook of suitably normalized articulatory parameters is used, and in the second stage, real speech data are used to further improve the mapping. During synthesis, neural networks are selected by dynamic programming using a criterion that ensures smoothly varying vocal tract shapes while maintaining a good spectral match. The method is able to accommodate nonuniqueness in acoustic‐to‐articulatory mapping and can be bootstrapped efficiently from natural speech. Results on the performance of this procedure compared to other mapping procedures, including codebook look‐up and a single multilayered network, are presented.

Novel Transmit Beamforming Schemes for Time-Selective Fading Multiantenna Systems

Abstract: Transmit beamforming has been widely adopted for wireless systems with multiple transmit antennas. For a block fading channel, the Grassmannian beamformer has been shown to provide very good performance for finite rate feedback. However, the original Grassmannian beamformer does not take the time domain correlation of the channel fading into consideration. In this paper, based on a first-order autoregressive (AR1) dynamic fading model, we develop two new classes of beamforming algorithms that exploit the interframe correlations in the channel fading. We first introduce an algorithm based on a standard predictive vector quantization (PVQ) approach, and the resulting PVQ beamformer accomplishes superior power delivery at the receiver. However, the error performance of the PVQ beamformer is not satisfactory at high signal-to-noise ratios, and it also has a high implementation complexity. To resolve these issues, we then develop a novel successive beamforming (SBF) algorithm. The new SBF scheme uses the knowledge of the previous fading blocks to aid the beamforming codebook design of the current fading block. The beamforming codebook is constructed based on the successive partition of the surface of a spherical cap. The new SBF scheme accomplishes nearly the same performance as that of the PVQ beamformer, and it has a much simpler implementation. Through numerical simulations, we demonstrate that the proposed beamformers outperform the other previously proposed beamformers at various fading scenarios