This paper proposes a novel speech denoising method based on tensor filtering, in which the microphone array speech signal is constructed by tensor data and processed by tensor filtering model. The multi-microphone signal is represented with three-order tensor space in the way of channel, time and frequency. Noise can be reduced by finding the lower-rank approximation of the three-order tensor with tucker model. MDL (Minimum Description Length) criterion is used to estimate the optimal tensor rank. The performance of the proposed approach is evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed approach has potential ability of retrieving the target signal from noisy microphone array signal.

为了提高单通道语音增强降噪算法的整体质量,该文从噪声消除和语音感知两个角度出发对传统语音增强算法进行改进,通过引入多种处理手段来达到最佳优化效果.首先在参数估计方面,把基于弱语音出现的平滑算法加入到基于固定先验信噪比的软判决方法中来解决噪声谱过估计问题,并根据语音帧存在概率动态调整平滑因子,从而提高先验信噪比的跟踪效果.其次在语音质量感知提升方面,采用谐波恢复的方法重建语音段的高频谐波分量,并采用相位补偿和增益平滑的方法消除静默段和语音段的音乐噪声.实验结果表明,相比传统算法,该文算法通过引入参数估计改进模块和感知质量提升模块,在消噪效果和语音质量两方面均得到了较大的提高,并适用于多类噪声环境和信噪比条件.

The theory of layered space-time code,space-time block code,space-time trellis code were introduced, and the technology of combing STC and OFDM was researched.Then,several MIMO system models were built in different STC conditions and their BER performances were simulated. The simulation results were analyzed. Finally, the advantages and disadvantages of three kinds of STC and the advantages of STC-OFDM were summarized. Some other application combined with STC such as cooperative relay transmission system and the prospect of STC were discussed.

This paper proposes a tensor-preprocessing multi-microphone signal subspace approach for speech enhancement. The approach includes two parts to eliminate the noise in multi-microphone system step by step including tensor part and subspace part. Noise is preliminarily reduced in part by finding the lower-rank approximation of a three-order tensor constructed from the multi-microphone signal with tucker model in tensor part. Speech enhancement is finished by a linear filter estimated from the data covariance matrix and the estimated noise variance in subspace part. The performance of the proposed approach is evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed approach has good performance of retrieving the target signal from noisy multi-microphone signal.

Speech enhancement is an effective method to solve noise pollution,the main target is to extract pure voice from the noisy speech as much as possible,and it has been an important research field in speech processing and has an important value in practice.Actually,the mainly often used algorithms include Spectral Subtraction,Wiener Filter,the algorithms based on statistical model(e.g.MMSE) and Subspace Decomposition.This paper mainly discusses the basic principles of spectral subtraction,MMSE,two-step noise reduction(TSNR) and subspace decomposition and also makes comparisons and analysis on the performance of noise reduction among the four algorithms by using objective assessment index.