AVAudioRecorder 通过将 FFT 加速到频率 - 执行
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【中文标题】AVAudioRecorder 通过将 FFT 加速到频率 - 执行【英文标题】:AVAudioRecorder through accelerate FFT into frequency - EXECUTION 【发布时间】:2012-05-26 22:11:29 【问题描述】:我的主要目标:找到通过 AVAudioRecorder 引入的噪音的频率。我遵循了这个:
http://www.ehow.com/how_12224909_detect-blow-mic-xcode.html
我已经阅读了许多关于 SO 的问题,询问如何检测频率。大多数答案说:“使用 FFT!”然后提问者说:“哦,太好了!”。
我的问题是,你如何从这里得到:
- (void)levelTimerCallback
[recorder updateMeters];
const double ALPHA = 0.05;
double peakPowerForChannel = pow(10, (0.05 * [recorder peakPowerForChannel:0]));
lowPassResults = ALPHA * peakPowerForChannel + (1.0 - ALPHA) * lowPassResults;
if (lowPassResults > sensitivitySlider.value)
NSLog(@"Sound detected");
//What goes here so I can spit out a frequency?
以某种方式神奇地使用 FFT...(我将使用加速.h),
然后以“频率 = 450.3”结束?
如果有人可以告诉我我将使用的实际代码
-
将声音从 AVAudioRecorder 插入 Accelerate
和
如何将结果转化为频率...
那将不胜感激。
提前致谢。
【问题讨论】:
许多重复项,例如FFT Pitch Detection for ios using Accelerate Framework? 【参考方案1】:没有什么“去那里”,因为 AVRecorder API 没有插入 Accelerate 框架。相反,您必须使用完全不同的 API,Audio Queue 或 RemoteIO Audio Unit API,来捕获音频输入,完全不同的代码安排,例如等待回调获取数据,缓冲区大小管理以获取数据数组合适的大小来馈送 FFT,然后知道足够的 DSP 来对您正在寻找的特定频率测量类型的 FFT 结果进行后处理。
【讨论】:
【参考方案2】:嗯,事实证明有些东西可以“去那里”。我没有使用 Accelerate,而是在亚马逊上买了一本关于傅立叶分析的书,并用它来构建我自己的 FFT。它输出的不是单个频率,而是许多频率中每个频率的电平,这基本上是我想要的。
这是我的 FFT 计算课程:
class FFTComputer: NSObject
class func integerBitReverse(_ input:Int,binaryDigits:Int) -> Int
return integerForReversedBooleans(booleansForInt(input, binaryDigits: binaryDigits))
class func integerForReversedBooleans(_ booleans:[Bool]) -> Int
var integer = 0
var digit = booleans.count - 1
while digit >= 0
if booleans[digit] == true
integer += Int(pow(Double(2), Double(digit)))
digit -= 1
return integer
class func Pnumber(_ k:Int,placesToMove:Int, gamma:Int) -> Int
var booleans = booleansForInt(k, binaryDigits: gamma)
for _ in 0 ..< placesToMove
booleans.removeLast()
booleans.insert(false, at: 0)
return integerForReversedBooleans(booleans)
class func booleansForInt(_ input:Int,binaryDigits:Int) -> [Bool]
var booleans = [Bool]()
var remainingInput = input
var digit = binaryDigits - 1
while digit >= 0
let potential = Int(pow(Double(2), Double(digit)))
if potential > remainingInput
booleans.append(false)
else
booleans.append(true)
remainingInput -= potential
digit += -1
return booleans
class func fftOfTwoRealFunctions(_ realX1:[CGFloat], realX2:[CGFloat], gamma:Int) -> (([CGFloat],[CGFloat]),([CGFloat],[CGFloat]))
let theFFT = fft(realX1, imaginaryXin: realX2, gamma: gamma)
var R = theFFT.0
var I = theFFT.1
let N = Int(pow(2.0, Double(gamma)))
var realOut1 = [CGFloat]()
var imagOut1 = [CGFloat]()
var realOut2 = [CGFloat]()
var imagOut2 = [CGFloat]()
for n in 0..<N
var Rback:CGFloat
var Iback:CGFloat
if n == 0
Rback = R[0]
Iback = I[0]
else
Rback = R[N-n]
Iback = I[N-n]
realOut1.append(CGFloat(R[n]/2 + Rback/2))
realOut2.append(CGFloat(I[n]/2 + Iback/2))
imagOut1.append(CGFloat(I[n]/2 - Iback/2))
imagOut2.append(-CGFloat(R[n]/2 - Rback/2))
return ((realOut1,imagOut1),(realOut2,imagOut2))
class func fft(_ realXin:[CGFloat], imaginaryXin:[CGFloat], gamma:Int) -> ([CGFloat],[CGFloat])
var realX = realXin
var imaginaryX = imaginaryXin
let N = Int(pow(2.0, Double(gamma)))
var N2 = N/2
var NU1 = gamma - 1 // Always equals (gamma - l)
var realWP:Double = 1
var imaginaryWP:Double = 0
var redoPCounter = 0
func redoP(_ k:Int, places:Int)
let P = Pnumber(k, placesToMove:places, gamma: gamma)
let inside = (-2*Double.pi*Double(P))/Double(N)
realWP = cos(inside)
imaginaryWP = sin(inside)
var l = 1
while l <= gamma
var k = 0
var I = 1
while k < N - 1
if redoPCounter == N2
redoP(k,places: NU1)
redoPCounter = 0
redoPCounter += 1
// Swift.print(realX.count,imaginaryX.count,k+N2)
let realT1 = (realWP*Double(realX[k + N2]))-(imaginaryWP*Double(imaginaryX[k + N2]))
let imaginaryT1 = (realWP*Double(imaginaryX[k + N2]))+(imaginaryWP*Double(realX[k + N2]))
realX[k+N2] = realX[k] - CGFloat(realT1)
imaginaryX[k+N2] = imaginaryX[k] - CGFloat(imaginaryT1)
realX[k] = realX[k] + CGFloat(realT1)
imaginaryX[k] = imaginaryX[k] + CGFloat(imaginaryT1)
k += 1
if I == N2
k += N2
I = 1
else
I += 1
N2 = N2/2
NU1 = NU1 - 1
redoPCounter = 0
realWP = 1
imaginaryWP = 0
l += 1
for k in 0 ..< N - 1
let i = integerBitReverse(k, binaryDigits:gamma)
if i > k
let placeholderReal = realX[k]
let placeholderImaginary = imaginaryX[k]
realX[k] = realX[i]
imaginaryX[k] = imaginaryX[i]
realX[i] = placeholderReal
imaginaryX[i] = placeholderImaginary
return (realX,imaginaryX)
class func magnitudeAndPhasePresentations(_ realX:[CGFloat], imaginaryX:[CGFloat]) -> ([CGFloat],[CGFloat])
var magnitudes = [CGFloat]()
var phases = [CGFloat]()
var lastMagnitude:CGFloat = 0
var lastPhase:CGFloat = 0
for n in 0 ..< realX.count
let real = realX[n]
let imaginary = imaginaryX[n]
if real != 0
lastMagnitude = sqrt(pow(real, 2)+pow(imaginary, 2))
lastPhase = atan(imaginary/real)
magnitudes.append(lastMagnitude)
phases.append(lastPhase)
return (magnitudes,phases)
class func magnitudePresentation(_ realX:[CGFloat], imaginaryX:[CGFloat]) -> [CGFloat]
var magnitudes = [CGFloat]()
var lastMagnitude:CGFloat = 0
for n in 0 ..< realX.count
let real = realX[n]
let imaginary = imaginaryX[n]
if real != 0
lastMagnitude = sqrt(pow(real, 2)+pow(imaginary, 2))
magnitudes.append(lastMagnitude)
return magnitudes
为了获得音频,我使用了 Novocaine:https://github.com/alexbw/novocaine
我建议阅读一些有关傅立叶变换的内容,但将 Novocaine(麦克风)中的数据插入 FFTComputer 并返回一些频率确实没有那么难。
(2 到 gamma 是 realXin 的计数。我本来可以计算 gamma,所以如果你想改变它,请继续。只需将 Novocaine 数据转换为 CGFloats 数组,将其放入 realXin,放入imagXin 中相同大小的空数组,然后输入正确的 gamma。然后,可以绘制输出以查看频率。)
【讨论】:
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