试图在 Python 中获取 .wav 文件的频率

Question

我知道关于 Python 中 .wav 文件的问题几乎被打死了，但我非常沮丧，因为似乎没有人的答案对我有用。我想做的事情对我来说似乎相对简单：我想确切地知道 .wav 文件中在给定时间的频率。我想知道，比如“从时间n毫秒到n+10毫秒，声音的平均频率是x赫兹”。我看到人们谈论傅立叶变换和 Goertzel 算法，以及各种模块，我似乎无法弄清楚如何去做我所描述的事情。我已经尝试查找诸如“在 python 中查找 wav 文件的频率”之类的内容大约 20 次，但均无济于事。有人可以帮我吗？

我正在寻找的是一个像这个伪代码这样的解决方案，或者至少一个可以做类似伪代码的解决方案：

import some_module_that_can_help_me_do_this as freq

file = 'output.wav'
start_time = 1000  # Start 1000 milliseconds into the file
end_time = 1010  # End 10 milliseconds thereafter

print("Average frequency = " + str(freq.average(start_time, end_time)) + " hz")

请假设（我相信你可以说）我是个数学白痴。这是我的第一个问题，所以要温柔

Answer 1

这个答案很晚，但你可以试试这个：

（注意：我不应该为此获得任何荣誉，因为我从其他 SO 帖子和这篇关于使用 Python 进行 FFT 的精彩文章中获得了大部分内容：https://realpython.com/python-scipy-fft/）

import numpy as np
from scipy.fft import *
from scipy.io import wavfile


def freq(file, start_time, end_time):

    # Open the file and convert to mono
    sr, data = wavfile.read(file)
    if data.ndim > 1:
        data = data[:, 0]
    else:
        pass

    # Return a slice of the data from start_time to end_time
    dataToRead = data[int(start_time * sr / 1000) : int(end_time * sr / 1000) + 1]

    # Fourier Transform
    N = len(dataToRead)
    yf = rfft(dataToRead)
    xf = rfftfreq(N, 1 / sr)

    # Uncomment these to see the frequency spectrum as a plot
    # plt.plot(xf, np.abs(yf))
    # plt.show()

    # Get the most dominant frequency and return it
    idx = np.argmax(np.abs(yf))
    freq = xf[idx]
    return freq

此代码适用于任何.wav 文件，但它可能略有偏差，因为它只返回最主要的频率，而且它只使用音频的第一个通道（如果不是单声道）。

如果您想了解有关傅里叶变换如何工作的更多信息，请观看 3blue1brown 提供的视频，并提供直观的解释：https://www.youtube.com/watch?v=spUNpyF58BY

Answer 2

我感到 OP 很沮丧 - 如果有人需要，不应该很难找到如何获取频谱图的值而不是查看频谱图图像：

#!/usr/bin/env python

import librosa
import sys
import numpy as np
import matplotlib.pyplot as plt
import librosa.display

np.set_printoptions(threshold=sys.maxsize)

filename = 'filename.wav'
Fs = 44100
clip, sample_rate = librosa.load(filename, sr=Fs)

n_fft = 1024  # frame length 
start = 0 

hop_length=512

#commented out code to display Spectrogram
X = librosa.stft(clip, n_fft=n_fft, hop_length=hop_length)
#Xdb = librosa.amplitude_to_db(abs(X))
#plt.figure(figsize=(14, 5))
#librosa.display.specshow(Xdb, sr=Fs, x_axis='time', y_axis='hz') 
#If to pring log of frequencies  
#librosa.display.specshow(Xdb, sr=Fs, x_axis='time', y_axis='log')
#plt.colorbar()

#librosa.display.waveplot(clip, sr=Fs)
#plt.show()

#now print all values 

t_samples = np.arange(clip.shape[0]) / Fs
t_frames = np.arange(X.shape[1]) * hop_length / Fs
#f_hertz = np.arange(N / 2 + 1) * Fs / N       # Works only when N is even
f_hertz = np.fft.rfftfreq(n_fft, 1 / Fs)         # Works also when N is odd

#example
print('Time (seconds) of last sample:', t_samples[-1])
print('Time (seconds) of last frame: ', t_frames[-1])
print('Frequency (Hz) of last bin:   ', f_hertz[-1])

print('Time (seconds) :', len(t_samples))

#prints array of time frames 
print('Time of frames (seconds) : ', t_frames)
#prints array of frequency bins
print('Frequency (Hz) : ', f_hertz)

print('Number of frames : ', len(t_frames))
print('Number of bins : ', len(f_hertz))

#This code is working to printout frame by frame intensity of each frequency
#on top line gives freq bins
curLine = 'Bins,'
for b in range(1, len(f_hertz)):
    curLine += str(f_hertz[b]) + ','
print(curLine)

curLine = ''
for f in range(1, len(t_frames)):
    curLine = str(t_frames[f]) + ','
    for b in range(1, len(f_hertz)): #for each frame, we get list of bin values printed
        curLine += str("%.02f" % np.abs(X[b, f])) + ','
        #remove format of the float for full details if needed
        #curLine += str(np.abs(X[b, f])) + ','
        #print other useful info like phase of frequency bin b at frame f.
        #curLine += str("%.02f" % np.angle(X[b, f])) + ',' 
    print(curLine)

Answer 3

如果您想检测pitch 的声音（看起来您确实这样做了），那么就 Python 库而言，您最好的选择是 aubio。请参考此example 进行实施。

import sys
from aubio import source, pitch

win_s = 4096
hop_s = 512 

s = source(your_file, samplerate, hop_s)
samplerate = s.samplerate

tolerance = 0.8

pitch_o = pitch("yin", win_s, hop_s, samplerate)
pitch_o.set_unit("midi")
pitch_o.set_tolerance(tolerance)

pitches = []
confidences = []

total_frames = 0
while True:
    samples, read = s()
    pitch = pitch_o(samples)[0]
    pitches += [pitch]
    confidence = pitch_o.get_confidence()
    confidences += [confidence]
    total_frames += read
    if read < hop_s: break

print("Average frequency = " + str(np.array(pitches).mean()) + " hz")

请务必查看docs 了解音高检测方法。

我还认为您可能对估计平均频率和其他一些音频参数感兴趣，而无需使用任何特殊库。让我们只使用numpy！这应该让您更好地了解如何计算此类音频特征。它基于来自seewave 包的specprop。检查文档以了解计算特征的含义。

import numpy as np

def spectral_properties(y: np.ndarray, fs: int) -> dict:
    spec = np.abs(np.fft.rfft(y))
    freq = np.fft.rfftfreq(len(y), d=1 / fs)
    spec = np.abs(spec)
    amp = spec / spec.sum()
    mean = (freq * amp).sum()
    sd = np.sqrt(np.sum(amp * ((freq - mean) ** 2)))
    amp_cumsum = np.cumsum(amp)
    median = freq[len(amp_cumsum[amp_cumsum <= 0.5]) + 1]
    mode = freq[amp.argmax()]
    Q25 = freq[len(amp_cumsum[amp_cumsum <= 0.25]) + 1]
    Q75 = freq[len(amp_cumsum[amp_cumsum <= 0.75]) + 1]
    IQR = Q75 - Q25
    z = amp - amp.mean()
    w = amp.std()
    skew = ((z ** 3).sum() / (len(spec) - 1)) / w ** 3
    kurt = ((z ** 4).sum() / (len(spec) - 1)) / w ** 4

    result_d = {
        'mean': mean,
        'sd': sd,
        'median': median,
        'mode': mode,
        'Q25': Q25,
        'Q75': Q75,
        'IQR': IQR,
        'skew': skew,
        'kurt': kurt
    }

    return result_d

Answer 4

尝试以下方法，它对我有用，我生成了一个频率为 1234 的正弦波文件 from this page.

from scipy.io import wavfile

def freq(file, start_time, end_time):
    sample_rate, data = wavfile.read(file)
    start_point = int(sample_rate * start_time / 1000)
    end_point = int(sample_rate * end_time / 1000)
    length = (end_time - start_time) / 1000
    counter = 0
    for i in range(start_point, end_point):
        if data[i] < 0 and data[i+1] > 0:
            counter += 1
    return counter/length    

freq("sin.wav", 1000 ,2100)
1231.8181818181818

已编辑：稍微清理一下 for 循环