Python中的频率分析

我正在尝试使用Python检索实时音频输入的主频。目前，我正在尝试使用我的笔记本电脑内置麦克风的音频流，但在测试以下代码时，我得到的结果非常糟糕

    # Read from Mic Input and find the freq's
    import pyaudio
    import numpy as np
    import bge
    import wave

    chunk = 2048

    # use a Blackman window
    window = np.blackman(chunk)
    # open stream
    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 1920

    p = pyaudio.PyAudio()
    myStream = p.open(format = FORMAT, channels = CHANNELS, rate = RATE, input = True, frames_per_buffer = chunk)

    def AnalyseStream(cont):
        data = myStream.read(chunk)
        # unpack the data and times by the hamming window
        indata = np.array(wave.struct.unpack("%dh"%(chunk), data))*window
        # Take the fft and square each value
        fftData=abs(np.fft.rfft(indata))**2
        # find the maximum
        which = fftData[1:].argmax() + 1
        # use quadratic interpolation around the max
        if which != len(fftData)-1:
            y0,y1,y2 = np.log(fftData[which-1:which+2:])
            x1 = (y2 - y0) * .5 / (2 * y1 - y2 - y0)
            # find the frequency and output it
            thefreq = (which+x1)*RATE/chunk
            print("The freq is %f Hz." % (thefreq))
        else:
            thefreq = which*RATE/chunk
            print("The freq is %f Hz." % (thefreq))

    # stream.close()
    # p.terminate()

这段代码是从中分解出来的，它处理波文件的傅里叶分析。它是在当前的模块化结构中，因为我在Blender游戏环境中实现它（因此顶部是import bge），但我非常确定我的问题在于AnalyseStream模块

如果您能提供任何建议，我们将不胜感激

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更新：我时不时地得到正确的值，但很少在错误的值中找到它们（Hello find the maximum computing）实时分析的FFT变得有点慢

如果你不想用复杂的波形来寻找频率，你可以使用任何基于时域的方法，比如过零，这样性能会更好

去年我做了一个简单的函数，通过过零来计算频率

#Eng Eder de Souza 01/12/2011
#ederwander
from matplotlib.mlab import find
import pyaudio
import numpy as np
import math


chunk = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 20


def Pitch(signal):
    signal = np.fromstring(signal, 'Int16');
    crossing = [math.copysign(1.0, s) for s in signal]
    index = find(np.diff(crossing));
    f0=round(len(index) *RATE /(2*np.prod(len(signal))))
    return f0;


p = pyaudio.PyAudio()

stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
output = True,
frames_per_buffer = chunk)

for i in range(0, RATE / chunk * RECORD_SECONDS):
    data = stream.read(chunk)
    Frequency=Pitch(data)
    print "%f Frequency" %Frequency

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ederwander

还有一个函数

scipy.signal.lombsargle

，用于计算Lomb Scarge周期图，该函数自v0.10.0起可用。该方法即使适用于采样不均匀的信号。似乎必须减去数据的平均值，该方法才能正常工作，尽管文档中未提及自我陶醉。 更多信息可在scipy参考指南中找到：

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1920的采样率看起来有点可疑。更典型的音频采样率是8000或44100。您使用哪种声音进行正确性测试？如果不是来自正弦波发生器，您听到的音调和频率峰值可能会非常不同。