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# 信号处理代写signal processing代考|Speech recognition

##### 代写数字信号处理signal processing

In comparison, the output viewpoint examines how a single point in the output signal is determined by the various values from the input signal. Just as with discrete signals, each instantaneous value in the output signal is affected by a section of the input signal, weighted by the impulse response flipped left-for-right. In the discrete case, the signals are multiplied and summed. In the continuous case, the signals are multiplied and integrated. In equation form:

$$y(t)=\int_{-\infty}^{+\infty} x(\tau) h(t-\tau) d \tau$$

The convolution integral. This equation defines the meaning of: $y(t)=x(t) * h(t)$.

This equation is called the convolution integral, and is the twin of the convolution sum ) used with discrete signals.shows how this equation can be understood. The goal is to find an expression for calculating the value of the output signal at an arbitrary time, $t$. The first step is to change the independent variable used to move through the input signal and the impulse response. That is, we replace $t$ with $\tau$ (a lower case Greek tau). This makes $x(t)$ and $h(t)$ become $x(\tau)$ and $h(\tau)$, respectively. This change of variable names is needed because $t$ is already being used to represent the point in the output signal being calculated. The next step is to flip the impulse response left-for-right, turning it into $h(-\tau)$. Shifting the flipped impulse response to the location $t$, results in the expression becoming $h(t-\tau)$. The input signal is then weighted by the flipped and shifted impulse response by multiplying the two, i.e., $x(\tau) h(t-\tau)$. The value of the output signal is then found by integrating this weighted input signal from negative to positive infinity.

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• 微分方程 Differential equations
• 递归关系 Recurrence relations
• 变换理论 Time-frequency analysis – for dealing with non-stationary signals [14]
• 时频分析 Transformation theory Time-frequency analysis – for dealing with non-stationary signals
• 频谱估计 Spectral estimation – for determining the spectral content
• 统计信号处理 Statistical signal processing – for analyzing and extracting information based on the stochastic properties of signals and noise
• 线性时不变系统理论和变换理论 Linear time-invariant systems theory and transformation theory
• 多项式信号处理 Polynomial signal processing – analysis of systems related to inputs and outputs using polynomials

## 信号处理代写signal processing代考|synthesis

In speech recognition, only the right part of the structure in Fig. 5 is used. A characteristic sound is used as an input, and the filter finds the coefficients $a_{1}, a_{2}, \ldots, a_{p}$, so that the average output squared error is minimised; this way, when the input is a delayed version of the sound, the estimator will output the sound itself – and therefore it is referred to as a predictor. A $p^{\text {th }}$-order predictor uses samples delayed by $1,2, \ldots, p$ time units to create its output; the predictor is a second order predictor.

## 信号处理代写SIGNAL PROCESSING代考|an AR model with parameters

1. Test the performance of such a predictor on real-world audio signals. Record your own voice corresponding to the sounds “e”, “a”,”s”, ” $\mathrm{t}$ ” and ” $\mathrm{x} “$. Consider the sampling frequency of $f_{s}=44100 \mathrm{~Hz}$ and $N=1000$ samples. Use each file as an input to the predictor and investigate the selection of the adaptation gain and the order of the predictor. Also consider gear shifting.
2. How would you find an optimal filter length? Suggest some heuristic and analytical approaches.
A standard measure for the performance of a predictor is the prediction gain
$$R_{p}=10 \log {10}\left(\frac{\sigma{x}^{2}}{\sigma_{e}^{2}}\right)$$
where $\sigma_{x}^{2}$ and $\sigma_{e}^{2}$ denote the variance of the input and error signals respectively.
3. Assess the performance of the predictor for each audio recording (typical prediction gain values are on the order of 1-25). Calculate and explain the prediction gains of the corresponding predictors for a 1000 -sample recording of your own speech, as in Part 4.5.1 above, but this time at the sampling frequency of $f_{s}=16000$ Hz. Comment on the relationship between the sampling frequency and the number of data samples in order to obtain quasi-stationary vowel sounds 6 , and for the learning curves to converge.

## 信号处理代写SIGNAL PROCESSING代考|AN AR MODEL WITH PARAMETERS

1. 在真实世界的音频信号上测试这种预测器的性能。录制与声音“e”、“a”、“s”、”相对应的自己的声音吨“ 和 ”X“. 考虑采样频率Fs=44100 H和和ñ=1000样品。使用每个文件作为预测器的输入，并研究自适应增益的选择和预测器的阶数。还要考虑换档。
2. 您将如何找到最佳过滤器长度？建议一些启发式和分析方法。
预测器性能的标准度量是预测增益
$$R_{p}=10 \log {10}\left(\frac{\sigma {x}^{2}}{\sigma_{e}^{ 2}}\right)$$
在哪里σX2和σ和2分别表示输入和误差信号的方差。
3. 评估每个音频记录的预测器性能吨和r○F1−25. 计算并解释你自己语音的 1000 个样本记录的相应预测器的预测增益，如上面第 4.5.1 部分所述，但这次的采样频率为Fs=16000赫兹。评论采样频率与数据样本数之间的关系，以获得准平稳的元音 6 ，并使学习曲线收敛。

## Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。