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196 lines
54 KiB

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 采样\n",
"__采样(sampling)即每隔一段时间在模拟声音信号的波形\n",
"上采集一个幅度值。下面一段代码绘图显示了一段鸟鸣声的模拟声音信号__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import interpolate\n",
"x = np.asarray([1,2,3,4,5,6,7,8,9,10,11,12,13,14])\n",
"y = np.asarray([5,2,5.4,12,9.8,6.2,5,10,14,13.2,8,6,12,8])\n",
"xnew =np.arange(1,14,0.1)\n",
"\n",
"#实现函数\n",
"func = interpolate.interp1d(x,y,kind='quadratic')\n",
"\n",
"#利用xnew和func函数生成ynew,xnew数量等于ynew数量\n",
"ynew = func(xnew)\n",
"\n",
"plt.plot(xnew,ynew)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 量化\n",
"__对其采样时,在波形信号上按时间维度等距离地选取 若干个离散的点,如下图所示所示。这些采样得到的幅 度值被记录下来。__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(xnew,ynew)\n",
"plt.scatter(x,y)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__第 3 个采样点的真实幅度值约为 5.4, 将其四舍五入近似到级数值 5;第 5 个采样点的真实幅度值约为9.8,将其四舍五入近似到级数值 10。同理可将第 6、第 10 个采样点的真实幅度值近似到相应的级数值。__"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function matplotlib.pyplot.show(*args, **kw)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x,y)\n",
"plt.grid()\n",
"plt.show"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function matplotlib.pyplot.show(*args, **kw)>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"y = np.asarray([5,2,5,12,9.5,6.2,5,10,14,13,8,6,12,8])\n",
"plt.scatter(x,y)\n",
"plt.grid()\n",
"plt.show"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 编码\n",
"__经过采样和量化,模拟声音信号转化为一组二进制数序\n",
"列,再通过编码将其按照一定的规则记录下来。采用不同的 编码方法,会形成不同格式的音频文件,如 WAV 格式、MP3 格式等。\n",
"通过手机、数码相机、数码摄像机等数字设备,可以拍摄 鸟类的照片,得到图像文件。图像数字化的过程和声音数字化 类似,都会经历采样、量化和编码三个步骤。__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}