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Speech analysis techniques useful for low or variable bit rate coding

机译:语音分析技术可用于低或可变比特率编码

摘要

We investigate, improve and develop speech analysis techniques which can be used to enhance various speech processing systems, especially low bit rate or variable bit rate coding of speech.The coding technique based on the sinusoidal representation of speech is investigated and implemented. Based on this study of the sinusoidal model of speech, improved analysis techniques to determine voicing, pitch and spectral estimation are developed, as well as noise reduction technique. We investigate the properties and limitations of the spectral envelope estimation vocoder (SEEVOC). We generalize, optimize and improve the SEEVOC and also compare it with LP in the presence of noise.The properties and applications of morphological filters for speech analysis are investigated. We introduce and investigate a novel nonlinear spectral envelope estimation method based on morphological operations, which is found to be very robust against noise. This method is also compared with the SEEVOC method. A simple method for the optimum selection of the structuring set size without using prior pitch information is proposed for many purposes. The morphological approach is then used for a new pitch estimation method and for the general sinusoidal analysis of speech or audio. Many of the new methods are based on a novel systematic analysis of the peak features of signals, including the study of higher order peaks.We propose a novel peak feature algorithm, which measure the peak characteristics of speech signal in time domain, to be used for end point detection and segmentation of speech. This nonparametric algorithm is flexible, efficient and very robust in noise. Several simple voicing measures are proposed and used in a new speech classifier. The harmonic-plus-noise decomposition technique is improved and extended to give an alternative to the methods used in the sinusoidal analysis method. Its applications to pitch estimation, speech classification and noise reduction are investigated.
机译:我们研究,改进和发展了可用于增强各种语音处理系统的语音分析技术,尤其是语音的低比特率或可变比特率编码。研究并实现了基于语音正弦表示的编码技术。基于对语音正弦模型的研究,开发了用于确定语音,音调和频谱估计的改进分析技术以及降噪技术。我们研究了频谱包络估计声码器(SEEVOC)的特性和局限性。我们对SEEVOC进行了概括,优化和改进,并在存在噪声的情况下将其与LP进行了比较。研究了形态学滤波器在语音分析中的特性和应用。我们介绍并研究了一种新的基于形态学运算的非线性频谱包络估计方法,该方法对噪声非常鲁棒。此方法也与SEEVOC方法进行了比较。出于多种目的,提出了一种在不使用现有间距信息的情况下最佳选择结构化组尺寸的简单方法。然后将形态学方法用于新的音高估计方法以及语音或音频的一般正弦分析。许多新方法都基于对信号峰值特征的新颖系统分析,包括对高阶峰值的研究。我们提出了一种新颖的峰值特征算法,该算法可在时域内测量语音信号的峰值特征,以供使用。用于端点检测和语音分割。这种非参数算法灵活,高效并且在噪声方面非常强大。提出了几种简单的发声措施并将其用于新的语音分类器中。改进了谐波加噪声分解技术,为正弦分析方法中使用的方法提供了一种替代方法。研究了其在音高估计,语音分类和降噪中的应用。

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