In this letter, a timing-offset estimation scheme is proposed for cooperative networks. The estimation scheme consists of coarse timing-offset estimation and fine timing-offset estimation. The presented scheme relies on periodic training data and linear mean square estimation for efficient estimation. The simulation results indicate that the performance of the proposed approach is better than or comparable to that of the conventional methods with lower computational complexity in the fine estimation.

In this paper, a revised method is proposed in order to determine the parameters of an F0 generation model from the observed F0 contour automatically. Compared with the previous method, there are two points revised in the proposed method. Firstly, we relax the endpoint constraint in the dynamic programming method, especially we allow the timing of the first phrase command to be earlier than the beginning point of the actual F0 pattern. Secondly, the z-transform method is introduced to convert the equation of the F0 model in order to simplify the calculation and save the computation time. An experiment with 100 sentences spoken by two males and two females selected from the speech database "ATR 503 sentences" has shown that the proposed method is effective as we expected.

Sinusoidal representation has been widely applied to speech modification, low bit rate speech and audio coding. Usually, speech signal is analyzed and synthesized using the overlap-add algorithm or the peak-picking algorithm. But the overlap-add algorithm is well known for high computational complexity and the peak-picking algorithm cannot track the transient and syllabic variation well. In this letter, both algorithms are applied to speech analysis/synthesis. Peaks are picked in the curve of power spectral density for speech signal; the frequencies corresponding to these peaks are arranged according to the descending orders of their corresponding power spectral densities. These frequencies are regarded as the candidate frequencies to determine the corresponding amplitudes and initial phases according to the least mean square error criterion. The summation of the extracted sinusoidal components is used to successively approach the original speech signal. The results show that the proposed algorithm can track the transient and syllabic variation and can attain the good synthesized speech signal with low computational complexity.

In this paper, we propose a fast fractal image coding based on LMSE (least mean square error) analysis and subblock feature. The proposed method focuses on efficient search of contrast scaling, position of its matched domain block, and isometric transform for a range block. The contrast scaling and the domain block position are searched using a cost function that comes from the LMSE analysis of the range block and its fractal-approximated block. The isometric transform is searched using 2 2 blocks formed with the averages of subblocks of range block and domain block. Experimental results show that the encoding time of a conventional fractal image coding with our search method is 25.6-39.7 times faster than that with full search method at the same bit rate while giving PSNR decrement of 0.2-0.7 dB with negligible deterioration in subjective quality. It is also shown that the encoding time of a conventional fractal image coding with our search method is 3.4-4.2 times faster than Jacquin's fractal image coding and is superior by maximum 0.8 dB in PSNR. It also yields reconstructed images of better quality.