改进的噪声鲁棒语音稀疏线性预测算法

语音线性预测分析算法在噪声环境下性能会急剧恶化,针对这一问题,提出一种改进的噪声鲁棒稀疏线性预测算法。首先采用学生t分布对具有稀疏性的语音线性预测残差建模,并显式考虑加性噪声的影响以提高模型鲁棒性,从而构建完整的概率模型。然后采用变分贝叶斯方法推导模型参数的近似后验分布,最终实现噪声鲁棒的稀疏线性预测参数估计。实验结果表明,与传统算法以及近几年提出的基于l_1范数优化的稀疏线性预测算法相比,该算法在多项指标上具有优势,对环境噪声具有更好的鲁棒性,并且谱失真度更小,因而能够有效提高噪声环境下的语音质量。     

一种对加性噪声和信道函数联合补偿的模型估计方法

语音识别系统在面对实际环境中多变的加性噪声和信道差异的影响时性能急剧下降,抑制这些噪声和差异所造成的性能下降具有重要意义。作者提出了一种模型补偿算法,使用句子中的非语音段估计加性噪声,然后利用EM算法估计信道函数,从而在倒谱域上对失配的声学模型进行联合补偿。实验表明,相比基线系统,采用该算法的系统的平均性能相对提升幅度超过50%。算法可以动态跟踪环境的变化,性能表现优于一些传统的语音识别稳健性处理算法。     

An improved algorithm for noise-robust sparse linear prediction of speech

The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments.To tackle this issue,an improved noise-robust sparse linear prediction algorithm is proposed.First,the linear prediction residual of speech is modeled as Student-t distribution,and the additive noise is incorporated explicitly to increase the robustness,thus a probabilistic model for sparse linear prediction of speech is built.Furthermore,variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters,and then the optimal linear prediction parameters are estimated robustly.The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l₁ norm minimization based sparse linear prediction algorithm proposed in recent years.Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.     

一种利用多带激励模型改进的低速率线性预测语音编码算法

提出了一种利用多带激励(MBE)模型改进的低速率线性预测(LPC)语音编码算法.文中介绍了该算法的参数提取、量化编码及语音合成的具体方案,并用C语言构造了一个基于该算法的速率为1,6 kb/s的语音编码/解码系统.主观测听表明,该系统性能与 2.4 kb/s的 MELP算法接近或相当。     

Estimating speech spectra for copy synthesis by linear prediction and by hand

Linear prediction is a widely available technique for analyzing acoustic properties of speech, although this method is known to be error-prone. New tests assessed the adequacy of linear prediction estimates by using this method to derive synthesis parameters and testing the intelligibility of the synthetic speech that results. Matched sets of sine-wave sentences were created, one set using uncorrected linear prediction estimates of natural sentences, the other using estimates made by hand. Phoneme restrictions imposed on linguistic properties allowed comparisons between continuous and intermittent voicing, oral or nasal and fricative manner, and unrestricted phonemic variation. Intelligibility tests revealed uniformly good performance with sentences created by hand-estimation and a minimal decrease in intelligibility with estimation by linear prediction due to manner variation with continuous voicing. Poorer performance was observed when linear prediction estimates were used to produce synthetic versions of phonemically unrestricted sentences, but no similar decline was observed with synthetic sentences produced by hand estimation. The results show a substantial intelligibility cost of reliance on uncorrected linear prediction estimates when phonemic variation approaches natural incidence.     

语谱图共振峰的自动跟踪算法

本文介绍一个共振峰轨迹的自动跟踪算法，其特点是不借助于其它的信息来源，仅仅是基于语谱图信息，来确定语谱图上前四个共振峰频率的位置和它们关于时间轴的轨迹。算法由三个层面构成；第一层面是进行频率分布的分析，以决定一个最佳的共振峰搜索起始位置；第二层面是采用双搜索算法，来跟踪随时间变化的共振峰轨迹；第三层面是解决某些冲突现象，在共振峰跟踪问题上的难点之一是处理多个共振峰的合并现象和冗余峰值现象。     

Using the method of modified linear prediction for robust acoustic coding of speech

Ensuring the acceptable acoustic coding of a signal under the conditions of noise interference is an important problem of recognizing speech signals. The result of calculating acoustic characteristics by the existing algorithms strongly depends on the signal-to-noise ratio. To solve the problem related to ensuring robust acoustic coding, we propose an algorithm for obtaining cepstral coefficients on the basis of the method of modified linear prediction. The algorithm is aimed at the specific features of the vocalized speech signals with a pronounced formant structure including the case of wideband additive interference. The algorithm stability as a function of the noise level is studied experimentally. The result is improved compared with the well-known approaches. __________ Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 49, No. 7, pp. 593–598, July 2006.     

基于氨基酸位置特异性的蛋白质Loop区结构预测改进方法

蛋白质loop区的结构预测是理解蛋白质功能的重要一环,而长loop区的结构预测至今还是生物信息学中的难题.目前己经出现了多种loop结构的算法,其中LEAP是预测精度最高的算法之一,但它在长loop区初始主链构象采样上仍有较大的改进余地.本文中我们将蛋白质二级结构预测算法SPINE X与LEAP算法结合起来,构建了新的主链扭转角分布图(拉氏图),在主链初始构象采样中引入氨基酸在蛋白序列中的位置特异性信息,使得初始构象的采样更具针对性,对取自CASP10单链蛋白的loop测试集的分析表明,对长度为10,11,12个氨基酸的长loop区,改进后算法都比原始LEAP算法的预测精度有显著提升.这种引入氨基酸位置特异性从而提高预测精度的思路有望进一步推广至loop结构预测的其他算法.     

An algorithm for sparse MRI reconstruction by Schatten p-norm minimization

In recent years, there has been a concerted effort to reduce the MR scan time. Signal processing research aims at reducing the scan time by acquiring less K-space data. The image is reconstructed from the subsampled K-space data by employing compressed sensing (CS)-based reconstruction techniques. In this article, we propose an alternative approach to CS-based reconstruction. The proposed approach exploits the rank deficiency of the MR images to reconstruct the image. This requires minimizing the rank of the image matrix subject to data constraints, which is unfortunately a nondeterministic polynomial time (NP) hard problem. Therefore we propose to replace the NP hard rank minimization problem by its nonconvex surrogate — Schatten p-norm minimization. The same approach can be used for denoising MR images as well.Since there is no algorithm to solve the Schatten p-norm minimization problem, we derive an efficient first-order algorithm. Experiments on MR brain scans show that the reconstruction and denoising accuracy from our method is at par with that of CS-based methods. Our proposed method is considerably faster than CS-based methods.     

An improved algorithm for computing topological entropy

A new algorithm is presented for computing the topological entropy of a unimodal map of the interval. The accuracy of the algorithm is discussed and some graphs of the topological entropy which are obtained using the algorithm are displayed.     

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.