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Elimination of unwanted signals in audio materials using wavelet transform

机译:使用小波变换消除音频材料中的不需要的信号

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As a sound engineer, employed at Croatian Radio, I realized that the tools for the reconstruction and restoration of old recordings (or damaged recordings) are insufficient and at least they have deficient analyzing method. Orthogonal frequency-based systems, such as the DFT do not offer a good insight into the temporal localization of unwanted parts of the audio material such as pulse-formed signal (e.g., clicks). In this search for such a tool, I reached for the discrete wavelet transformation (DWT) which is realized by double decomposition in order to obtain wavelet coefficients and a graphic depiction of the coefficients. Distortion is measured with according mean square error (MSE) and it is compared with the number of discarded wavelet coefficients. It is also done the comparison between DWT and the results obtained with frequency based systems based on discrete Fourier transform (DFT). After being performed on an arbitrary mathematical function, the wavelet transform is applied on a sound example. (Wave 22.05 kHz, 8 bit). It is shown that DWT is dealing very well with both, noise and discrete disturbance which are the most common problems in the daily work with audio material.
机译:作为在克罗地亚广播电台采用的声音工程师,我意识到旧录音(或损坏录音)的重建和恢复的工具是不够的,至少它们具有缺陷的分析方法。基于正交的基于频率的系统,例如DFT不提供对音频材料的不需要部分的时间定位的良好洞察,​​例如脉冲形成的信号(例如,点击)。在此搜索这样的工具中,我达到了通过双分解实现的离散小波变换(DWT),以便获得小波系数和系数的图形描绘。根据平均方误差(MSE)测量失真,与丢弃的小波系数的数量进行比较。在基于离散的傅里叶变换(DFT)的基于频率的系统中,还可以进行DWT与结果的比较。在对任意数学函数上执行之后,在声音示例上应用小波变换。 (波22.05 kHz,8位)。结果表明,DWT与噪声和离散扰动都很好,这是日常工作中最常见的问题。

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