Question

我正在研究Takuya Fujishima提出的Pitch Class Profile。我已经尽力实现这个等式（使用scipy和numpy）;但是，我得到了一些相当奇怪的结果。我已经讨论过如何将它放在DSP上，但我认为这更像是一个编码问题，而不是方程理解问题。

无论如何，这是我的代码。

import scipy.io.wavfile
import numpy as np
import math
import sys

class PCP:

    def __init__(self):
        self.note_references = [16.35, 17.32, 18.35, 19.45, 20.60, 21.83, 23.12, 24.50, 25.96, 27.50, 29.14, 30.87]
        self.results = {}


    def create_fft(self, filename):
        self.rate, self.data = scipy.io.wavfile.read('fmin.wav')
        print "Data from the File: \n", self.data

        self.frames = self.data.size
        print "Number of Frames: ", self.frames

        print "Rate: ", self.rate

        self.fft_results = np.fft.rfft(self.data) ##fft computing and normalization
        print "Results from the FFT: \n", self.fft_results


    # The work of the following classes was almost entirely based on a
    # thread in DSP.  Here is the link to the particular article
    # http://dsp.stackexchange.com/questions/13722/pitch-class-profiling
    # This function returns the values of the notes given the spectrograph
    def m_func(self, l, p):
        #M(l) = round(12 * log_2( (f_s*l)/(N*f_ref) ) ) % 12
        #print "L: ", l
        #print "Note: ", p
        a = self.rate * l
        b = self.frames * self.note_references[p]
        c = 12 * np.log2(a/b)
        d = np.round(c)
        e = np.mod(d.all(), 12)
        #print "Result: ", e
        #raw_input()
        return e


    def pcp(self, p):
        r = 0
        for l in self.fft_results:
            result = self.m_func(l[0], p)
            #print "actual returned result", result
            if result == p:
                r+=1
                #print "There was a match!  Add it!"
        return r


    def calculate_PCP(self):
        for p in range(0,11): #for all 12 notes
            self.results[p] = self.pcp(p)


    def print_results(self):
        for i in self.results.keys():
            print i , ":" , self.results[i]


def main():
    m = PCP()
    m.create_fft("fmin.wav")
    m.calculate_PCP()
    m.print_results()


if __name__ == '__main__':
    main()

以下是输出：

Data from the File: 
[[16 15]
 [ 9  9]
 [15 15]
 ..., 
 [ 0  0]
 [ 0  0]
 [ 0  0]]
Number of Frames:  352800
Rate:  44100
Results from the FFT: 
[[ 31.+0.j   1.+0.j]
 [ 18.+0.j   0.+0.j]
 [ 30.+0.j   0.+0.j]
 ..., 
 [  0.+0.j   0.+0.j]
 [  0.+0.j   0.+0.j]
 [  0.+0.j   0.+0.j]]
PCP.py:36: RuntimeWarning: divide by zero encountered in log2
  c = 12 * np.log2(a/b)
PCP.py:36: RuntimeWarning: invalid value encountered in cdouble_scalars
  c = 12 * np.log2(a/b)
0 : 143
1 : 176263
2 : 0
3 : 0
4 : 0
5 : 0
6 : 0
7 : 0
8 : 0
9 : 0
10 : 0

该文件包含一个弹奏F小调和弦的钢琴（在结果词典中以0,5和7作为回应）。然而，结果表明C＃/ Db非常强大，我当然可以确认录音中没有C＃。我非常感谢所有人的帮助！

Answer 1

音高频率与频谱频率不同，因此不等于每第12个fft幅度结果箱的内容（特别是对于实际音乐声音的录音）。如果不出意外，任何强大的奇次谐波（不是2的幂）将最终出现在错误的音高等级箱中。

所引用的算法仅适用于受限制的波形类别，这些波形可能无法代表现场音乐音频。

Pitch类分析的Python代码

1 个答案: