我意识到 Hellium3 确实让我了解了音高是什么，以及使用 Swift 做这些事情是否是个好主意。
我的问题最初是关于敲击 PCM 总线是否是从麦克风获取输入信号的方式。
自从问了这个问题后，我就做到了。使用通过点击 PCM 总线获得的数据并分析缓冲区窗口。
它工作得非常好，正是我对什么是 PCM 总线、缓冲区和采样频率缺乏了解，这让我首先提出了这个问题。
知道这三个可以更容易地看出这是正确的。
编辑：根据需要，我将粘贴我（已弃用）的 PitchDetector 实现。
class PitchDetector {
var samplingFrequency: Float
var harmonicConstant: Float
init(harmonicConstant: Float, samplingFrequency: Float) {
self.harmonicConstant = harmonicConstant
self.samplingFrequency = samplingFrequency
}
//------------------------------------------------------------------------------
// MARK: Signal processing
//------------------------------------------------------------------------------
func detectPitch(_ samples: [Float]) -> Pitch? {
let snac = self.snac(samples)
let (lags, peaks) = self.findKeyMaxima(snac)
let (τBest, clarity) = self.findBestPeak(lags, peaks: peaks)
if τBest > 0 {
let frequency = self.samplingFrequency / τBest
if PitchManager.sharedManager.inManageableRange(frequency) {
return Pitch(measuredFrequency: frequency, clarity: clarity)
}
}
return nil
}
// Returns a Special Normalision of the AutoCorrelation function array for various lags with values between -1 and 1
private func snac(_ samples: [Float]) -> [Float] {
let τMax = Int(self.samplingFrequency / PitchManager.sharedManager.noteFrequencies.first!) + 1
var snac = [Float](repeating: 0.0, count: samples.count)
let acf = self.acf(samples)
let norm = self.m(samples)
for τ in 1 ..< τMax {
snac[τ] = 2 * acf[τ + acf.count / 2] / norm[τ]
}
return snac
}
// Auto correlation function
private func acf(_ x: [Float]) -> [Float] {
let resultSize = 2 * x.count - 1
var result = [Float](repeating: 0, count: resultSize)
let xPad = repeatElement(Float(0.0), count: x.count - 1)
let xPadded = xPad + x + xPad
vDSP_conv(xPadded, 1, x, 1, &result, 1, vDSP_Length(resultSize), vDSP_Length(x.count))
return result
}
private func m(_ samples: [Float]) -> [Float] {
var sum: Float = 0.0
for i in 0 ..< samples.count {
sum += 2.0 * samples[i] * samples[i]
}
var m = [Float](repeating: 0.0, count: samples.count)
m[0] = sum
for i in 1 ..< samples.count {
m[i] = m[i - 1] - samples[i - 1] * samples[i - 1] - samples[samples.count - i - 1] * samples[samples.count - i - 1]
}
return m
}
/**
* Finds the indices of all key maximum points in data
*/
private func findKeyMaxima(_ data: [Float]) -> (lags: [Float], peaks: [Float]) {
var keyMaximaLags: [Float] = []
var keyMaximaPeaks: [Float] = []
var newPeakIncoming = false
var currentBestPeak: Float = 0.0
var currentBestτ = -1
for τ in 0 ..< data.count {
newPeakIncoming = newPeakIncoming || ((data[τ] < 0) && (data[τ + 1] > 0))
if newPeakIncoming {
if data[τ] > currentBestPeak {
currentBestPeak = data[τ]
currentBestτ = τ
}
let zeroCrossing = (data[τ] > 0) && (data[τ + 1] < 0)
if zeroCrossing {
let (τEst, peakEst) = self.approximateTruePeak(currentBestτ, data: data)
keyMaximaLags.append(τEst)
keyMaximaPeaks.append(peakEst)
newPeakIncoming = false
currentBestPeak = 0.0
currentBestτ = -1
}
}
}
if keyMaximaLags.count <= 1 {
let unwantedPeakOfLowPitchTone = (keyMaximaLags.count == 1 && data[Int(keyMaximaLags[0])] < data.max()!)
if unwantedPeakOfLowPitchTone {
keyMaximaLags.removeAll()
keyMaximaPeaks.removeAll()
}
let (τEst, peakEst) = self.approximateTruePeak(data.index(of: data.max()!)!, data: data)
keyMaximaLags.append(τEst)
keyMaximaPeaks.append(peakEst)
}
return (lags: keyMaximaLags, peaks: keyMaximaPeaks)
}
/**
* Approximates the true peak according to https://www.dsprelated.com/freebooks/sasp/Quadratic_Interpolation_Spectral_Peaks.html
*/
private func approximateTruePeak(_ τ: Int, data: [Float]) -> (τEst: Float, peakEst: Float) {
let α = data[τ - 1]
let β = data[τ]
let γ = data[τ + 1]
let p = 0.5 * ((α - γ) / (α - 2.0 * β + γ))
let peakEst = min(1.0, β - 0.25 * (α - γ) * p)
let τEst = Float(τ) + p
return (τEst, peakEst)
}
private func findBestPeak(_ lags: [Float], peaks: [Float]) -> (τBest: Float, clarity: Float) {
let threshold: Float = self.harmonicConstant * peaks.max()!
for i in 0 ..< peaks.count {
if peaks[i] > threshold {
return (τBest: lags[i], clarity: peaks[i])
}
}
return (τBest: lags[0], clarity: peaks[0])
}
}
所有功劳归功于 Philip McLeod，他的研究用于我上面的实现。http://www.cs.otago.ac.nz/research/publications/oucs-2008-03.pdf
