Arduino FFT库 - 版本历史

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Allblue：以“ Arduino FFT * ATmega328 @ 16MHz : Arduino UNO, Adafruit Pro Trink...”为内容创建页面

2017-03-29T02:46:00Z

以“ Arduino FFT  * ATmega328 @ 16MHz : Arduino UNO, Adafruit Pro Trink...”为内容创建页面

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Arduino FFT



* ATmega328 @ 16MHz : Arduino UNO, Adafruit Pro Trinket 5V, Adafruit Metro 328, Adafruit Metro Mini
* ATmega328 @ 12MHz : Adafruit Pro Trinket 3V
* ATmega32u4 @ 16MHz : Arduino Leonardo, Arduino Micro, Arduino Yun, Teensy 2.0
* ATmega32u4 @ 8MHz : Adafruit Flora, Bluefruit Micro
* ESP8266 : Adafruit Huzzah
* ATmega2560 @ 16MHz : Arduino Mega
* ATSAM3X8E : Arduino Due
* ATSAM21D : Arduino Zero, M0 Pro
* ATtiny85 @ 16MHz : Adafruit Trinket 5V
* ATtiny85 @ 8MHz : Adafruit Gemma, Arduino Gemma, Adafruit Trinket 3V

 

'''Examples''' 
 
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'''Arduino_FFT Functions''' 

- '''fft_run()'''
:- This is the main FFT function call. It takes no variables and returns no variables. It assumes there is a block of data already in SRAM, and that it is already re-ordered. The data is stored in array called fft_input[], which contains 2 16b values per FFT data point - one for real, the other for imaginary. If you are filling the array yourself, place the real values in the even bins, and the imaginary values in the odd bins. For example:
:*fft_input[0] = real1, fft_input[1] = imaginary1
:*fft_input[2] = real2, fft_input[3] = imaginary2
Therefore, there are 2 times as many points in the array as there are FFT bins. If you are using only real data (i.e. values sampled from the ADC), then put those values into the even numbered bins, and be sure to write 0 to the odd valued bins.
The final output is kept in fft_input[], with the even bins being the real magnitudes, and the odd bins being the imaginary magnitudes. The bins are in sequence of increasing frequncy. For example:
:*fft_input[0] & fft_input[1] = bin0 magnitudes (0hz -> Fs/N)
:*fft_input[2] & fft_input[3] = bin1 magnitudes (Fs/N -> 2Fs/N)
You will have to run one of the magnitude functions to get useful data from these bins.

- '''fft_reorder()'''
- This reorders the FFT inputs to get them ready for the special way in which the FFT algorithm processes data. Unless you do this yourself with another piece of code, you have to call this before running fft_run(). It takes no variables and returns no variables. This runs on the array fft_input[], so the data must be first filled into that array before this function is called.

- '''fft_window()'''
- This function multiplies the input data by a window function to help increase the frequency resolution of the FFT data. It takes no variables, and returns no variables. This processes the data in fft_input[], so that data must first be placed in that array before it is called. It must also be called before fft_reorder() or fft_run().

- '''fft_mag_lin8()'''
- This gives the magnitude of each bin in the FFT. It sums the squares of the imaginary and real parts, and then takes the square root, rounding the answer to 8b precision (it uses a lookup table, and scales the values to fit the full 8b range. It takes no variables, and returns no variables. It operates on fft_input[], and returns the data in an array called fft_lin_out8[]. You can then use the data in fft_lin_out8[]. The magnitude is only calculated for the first N/2 bins, as the second half of an FFT is identical to the first half for all real inputs. Therefore, fft_lin_out8[] has N/2 8b values, with each index representing the bin order. For example:
:*fft_lin_out8[0] = first bin magnitude (0hz -> Fs/N)
:*fft_lin_out8[1] = second bin magnitude (Fs/N -> 2Fs/N)
The output can be scaled to maximize the resolution using the SCALE factor. See the #define section for more detials.

- '''fft_mag_lin()'''
- This gives the magnitude of each bin in the FFT. It sums the squares of the imaginary and real, and then takes the square root. It uses a lookup table to calculate the square root, so it has limited precision. You can think of it as an 8b value times a 4b exponent. It covers the full 16b range, but only has 8b of precision at any point in that range. The data is taken in from fft_input[] and returned in fft_lin_out[]. The values are in sequential order, and there are only N/2 values total, as the FFT of a real signal is symetric about the center frequency.

- '''fft_mag_log()'''
- This gives the magnitude of each bin in the FFT. It sums the squares of the imaginary and real, and then takes the square root, and then takes the log base 2 of that value. Therefore, the output is compressed in a logarithmic fashion, and is essentially in decibels (times a scaling factor). It takes no variables, and returns no variables. It uses a lookup table to calculate the log of the square root, and scales the output over the full 8b range {the equation is 16*(log2((img2 + real2)1/2))}. It is only an 8b value, and the values are taken from fft_input[], and returned in fft_log_out[]. The output values are in sequential order of FFT frequency bins, and there are only N/2 total bins, as the second half of the FFT result is redundant for real inputs.

- '''fft_mag_octave()'''
- This outputs the RMS value of the bins in an octave (doubling of frequencies) format. This is more useful in some ways, as it is closer to how humans perceive sound. It doesn't take any variables, and doesn't return any variables. The input is taken from fft_output[] and returned in fft_oct_out[]. The data is represented as an 8b value of 16*log2(sqrt(mag)). There are LOG_N bins, and they are given as follows:
:*FFT_N = 256 : bins = [0, 1, 2:4, 5:8, 9:16, 17:32, 3:64, 65:128]
:*FFT_N = 128 : bins = [0, 1, 2:4, 5:8, 9:16, 17:32, 3:64]
Where, for example, (5:8) is a summation of all bins, 5 through 8. The data for each bin is squared, imaginary and real parts, and then added with all the squared magnitudes for the range. It is then divided down by the number of bins (which can be turned off - see #defines section), and the square root is taken, followed by the log being computed.

 



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←上一版本		2017年3月30日 (四) 09:15的版本
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−	Arduino FFT （FAST FOURIER TRANSFORM 快速傅里叶变换）	+	<font color="black" size="+1">'''[http://wiki.openmusiclabs.com/wiki/ArduinoFFT Arduino FFT] （FAST FOURIER TRANSFORM 快速傅里叶变换）Library'''</font>
		+
		+	Notes:
		+	:* For a fasher frequency analysis library, check out the [http://wiki.openmusiclabs.com/wiki/ArduinoFHT FHT]!
		+	:* The serial output of the examples is in binary, not ASCII. This means it will not be human readable on the serial port. Change serial.write() to serial.print() to fix this. You may need to write a for() loop to manually output each frequency bin.
		+
		+
		+	<font color="black" size="+1">'''About the Arduino FFT Library'''</font>
		+
		+	The Arduino FFT library is a fast implementation of a standard FFT algorithm which operates on only real data. It can give you up to 256 frequency bins at 16b depth, at a minimum of ~7ms update rate. It is adjustable from 16 to 256 bins, and has several output methods to suit various needs. It can be set to 16b linear, 8b linear, 8b logarithmic, or 8b octave output. All of these different modes are detailed in the read_me file (inside the FFT library folder). Since it takes in 16b, fixed point numbers, it has a noise floor of around -72dB in the low frequencies, and -76dB in the high frequencies. When using the onboard ADC, the ADC's noise floor is on the same order as the FFT's noise floor, giving somewhere between a 9b and 10b SNR (-55dB).
		+

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 - 这将以八度（频率倍增）格式输出机箱的RMS值。 这在某些方面更有用，因为它更接近于人类如何看待声音。 它不需要任何变量，也不返回任何变量。 输入取自fft_output []并在fft_oct_out []中返回。 数据表示为8b * log2（sqrt（mag））的8b值。 有LOG_N bin，它们给出如下：
-：* FFT_N = 256：bins = [0，1，2：4，5：8，9：16,17：32,3：64,65：128]
+:* FFT_N = 256：bins = [0，1，2：4，5：8，9：16,17：32,3：64,65：128]
-：* FFT_N = 128：bins = [0，1，2：4，5：8，9:16，17:32，3:64]
+:* FFT_N = 128：bins = [0，1，2：4，5：8，9:16，17:32，3:64]
  例如，（5：8）是5到8之间的所有仓的总和。每个仓的数据是平方，虚数和实数，然后加上范围的所有平方的数值。 然后将其除以数目（可以关闭 - 请参见#defines部分），并取平方根，然后计算日志。

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 - Sets the scaling factor for fft_mag_lin8(). Since 8b resolution is pretty poor, you will want to scale the values to max out the full range. Setting SCALE multiplies the output by a constant before doing the square root, so you have maximum resolution. It does consume slightly more resources, but is pretty minimal. SCALE can be any number from 1 -> 255. By default it is 1. 1, 2, 4, 128, and 256 consume the least resources.
-- 设置fft_mag_lin8 （） 的缩放因子。 由于8b分辨率很差，您将需要将值缩放到全范围内。 设置SCALE在执行平方根之前将输出乘以常数，因此您具有最大分辨率。 它消耗更多的资源，但是很少。 SCALE可以是从1 - > 255的任何数字。默认情况下，它是1，2，4，128和256消耗最少的资源。
+- 设置fft_mag_lin8()  的缩放因子。 由于8b分辨率很差，您将需要将值缩放到全范围内。 设置SCALE在执行平方根之前将输出乘以常数，因此您具有最大分辨率。 它消耗更多的资源，但是很少。 SCALE可以是从1 -> 255的任何数字。默认情况下，它是1，2，4，128和256消耗最少的资源。
@@ 第101行： / 第101行： @@
 - Turns on or off the window function resources. If you are not using fft_window(), then you should set WINDOW 0 (off). By default its 1 (on).
-- 打开或关闭窗口功能资源。 如果你不使用fft_window （） ，那么你应该设置WINDOW 0 （关闭） 。 默认情况下其1（开）。
+- 打开或关闭窗口功能资源。 如果你不使用fft_window() ，那么你应该设置WINDOW 0(off) 。 默认情况下其1（开）。
@@ 第107行： / 第107行： @@
 - Turns on or off the reorder function resources. If you are not using fft_reorder(), then you should set REORDER 0 (off). By default its 1 (on).
-- 打开或关闭重新排序功能资源。 如果你不使用fft_reorder （） ，那么你应该设置REORDER 0 （关闭） 。 默认情况下其1 （开 ）。
+- 打开或关闭重新排序功能资源。 如果你不使用fft_reorder() ，那么你应该设置REORDER 0(off) 。 默认情况下其1 （on ）。
@@ 第113行： / 第113行： @@
 - Turns on or off the log function resources. If you are using fft_mag_log(), then you should set LOG_OUT 1 (on). By default its 0 (off).
-- 打开或关闭日志功能资源。 如果你使用fft_mag_log （） ，那么你应该设置LOG_OUT 1 （on） 。 默认为0（关）。
+- 打开或关闭日志功能资源。 如果你使用fft_mag_log() ，那么你应该设置LOG_OUT 1(on) 。 默认为0（关）。
@@ 第119行： / 第119行： @@
 - Turns on or off the lin output function resources. If you are using fft_mag_lin(), then you should set LIN_OUT 1 (on). By default its 0 (off).
-- 打开或关闭lin输出功能资源。 如果你使用fft_mag_lin （） ，那么你应该设置LIN_OUT 1 （on） 。 默认为0（关）。
+- 打开或关闭lin输出功能资源。 如果你使用fft_mag_lin() ，那么你应该设置LIN_OUT 1(on) 。 默认为0（关）。
@@ 第125行： / 第125行： @@
 - Turns on or off the lin8 output function resources. If you are using fft_mag_lin8(), then you should set LIN_OUT8 1 (on). By default its 0 (off).
-- 打开或关闭lin8输出功能资源。 如果你使用fft_mag_lin8 （） ，那么你应该设置LIN_OUT8 1 （on） 。 默认为0（关）。
+- 打开或关闭lin8输出功能资源。 如果你使用fft_mag_lin8() ，那么你应该设置LIN_OUT8 1(on) 。 默认为0（关）。
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 - This turns on or off the octave output function resources. If you are using fft_mag_octave(), then you should set OCTAVE 1 (on). by default it is 0 (off).
-- 打开或关闭八度输出功能资源。 如果你使用fft_mag_octave （） ，那么你应该设置OCTAVE 1 （on） 。 默认为0（关）。
+- 打开或关闭八度输出功能资源。 如果你使用fft_mag_octave() ，那么你应该设置OCTAVE 1(on) 。 默认为0（关）。

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-Arduino FFT
+Arduino FFT （FAST FOURIER TRANSFORM 快速傅里叶变换 ）
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 <br>
 <font color="orange" size="+2">'''Arduino_FFT  Functions'''</font><br><br>
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 :-[[ XX]] :
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 <br>
 <font color="orange" size="+2">'''Arduino_FFT  Functions'''</font><br><br>
-<!-- <font color="dodgerblue" >'''BLACK 0'''</font> -->
 - <font color="forestgreen">'''fft_run()'''</font>
-:- This is the main FFT function call. It takes no variables and returns no variables. It assumes there is a block of data already in SRAM, and that it is already re-ordered. The data is stored in array called fft_input[], which contains 2 16b values per FFT data point - one for real, the other for imaginary. If you are filling the array yourself, place the real values in the even bins, and the imaginary values in the odd bins. For example:
+- This is the main FFT function call. It takes no variables and returns no variables. It assumes there is a block of data already in SRAM, and that it is already re-ordered. The data is stored in array called fft_input[], which contains 2 16b values per FFT data point - one for real, the other for imaginary. If you are filling the array yourself, place the real values in the even bins, and the imaginary values in the odd bins. For example:
 :*fft_input[0] = real1, fft_input[1] = imaginary1
 :*fft_input[2] = real2, fft_input[3] = imaginary2
@@ 第56行： / 第51行： @@
 - <font color="forestgreen">'''fft_mag_lin8()'''</font>
- - This gives the magnitude of each bin in the FFT. It sums the squares of the imaginary and real parts, and then takes the square root, rounding the answer to 8b precision (it uses a lookup table, and scales the values to fit the full 8b range. It takes no variables, and returns no variables. It operates on fft_input[], and returns the data in an array called fft_lin_out8[]. You can then use the data in fft_lin_out8[]. The magnitude is only calculated for the first N/2 bins, as the second half of an FFT is identical to the first half for all real inputs. Therefore, fft_lin_out8[] has N/2 8b values, with each index representing the bin order. For example:
+- This gives the magnitude of each bin in the FFT. It sums the squares of the imaginary and real parts, and then takes the square root, rounding the answer to 8b precision (it uses a lookup table, and scales the values to fit the full 8b range. It takes no variables, and returns no variables. It operates on fft_input[], and returns the data in an array called fft_lin_out8[]. You can then use the data in fft_lin_out8[]. The magnitude is only calculated for the first N/2 bins, as the second half of an FFT is identical to the first half for all real inputs. Therefore, fft_lin_out8[] has N/2 8b values, with each index representing the bin order. For example:
 :*fft_lin_out8[0] = first bin magnitude (0hz -> Fs/N)
 :*fft_lin_out8[1] = second bin magnitude (Fs/N -> 2Fs/N)
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