在

csdn上看到一篇博客“根據樂譜合成鋼琴音樂（https://blog.csdn.net/u011478373/article/details/60470332）”，寫得不錯，非常感興趣，就把博客中的Python代碼拷貝下來運行了一下，結果不行，原因是缺乏了一下關鍵參數定義，如：

1）wave_data

2）ampli

3）windowsize

分析了一下，將這幾個參數補充齊了，刪除了部分冗余代碼，現在程序可以運行了，可以用Python產生出鋼琴音色了，十分好聽。由于代碼可以運行和調試，可以幫助大家理解音樂生成的原理。下面幾張圖是生成的鋼琴聲的波形圖、聲譜圖、諧波特征和衰減特征。代碼在后面，大家可以下載運行試試，我用的是Python3.4。

諧波特征和時域衰減特征

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波形圖 聲譜圖

            
              import wave

import numpy as np
import math
import matplotlib.pyplot as plt

# TO DO: reform it into piano
#-----------------------------------------
#生成正弦波
def gen_sin(amp, f, fs, tau):
    #（開始值，結束值，個數）
    nT = np.linspace(0,tau, round(tau/(1.0/fs)))#根據步長生成數組，在指定的間隔內返回均勻間隔的數字，返回num個均勻分布的樣本，在[start, stop]。
    signal =np.array([amp*np.cos(2*np.pi*f*t) for t in nT])
    return signal

#model the harmonic feature in frequency domain
#1~15諧波與基頻的比例關系
Amp=[1,0.340,0.102,0.085,0.070,0.065,0.028,0.085,0.011,0.030,0.010,0.014,0.012,0.013,0.004]
numharmonic=len(Amp)#諧波個數

wave_data=np.array([0 for i in range(0,40000)])
wave_data = np.reshape(wave_data,[40000,1]).T
pianomusic=[0 for x in range(0,len(wave_data[0]))]
startpoint=0

#model the piano note attenuation feature in the time domain
#對每個鋼琴音的時域衰減建模
attenuation=[0 for x in range(0, 8000)]
#the attack stage
for i in range(0,200):
    attenuation[i]=i*0.005
#the attenuate stage
#衰減階段
for i in range(200,800):
    attenuation[i]=1-(i-200)*0.001
#the maintain stage
#保持階段    
for i in range(800,4000):
    attenuation[i]=0.4-(i-800)*0.000078
for i in range(4000,8000):
    attenuation[i]=0.15-(i-4000)*0.0000078

#compose each note in each time quantum
nomalizedbasicfreq=[261.63,261.63,261.63,261.63,293.665,293.665,293.665,293.665,329.628,329.628,329.628,329.628,349.228,349.228,349.228,349.228,391.995,391.995,391.995,391.995,440,440,440,440,493.883,493.883,493.883,493.883,523.251,523.251,523.251,523.251,587.33,587.33,587.33,587.33,659.255,659.255,659.255,659.255]
ampli=[(math.pow(2,2*8-1)-1) for i in range(0,40)]
#40個/4=10
notestime=[4,4,4,4,4,4,4,4,4,4]#10個
windowsize=1000
for w in range(0,len(notestime)):
    #計算音符時長
    #初始化音符為0
    pianonote = [0 for x in range(0, windowsize*notestime[w])] #get the length according to the time of the note
    #計算每一個諧音并累加
    for i in range(0, numharmonic): #get the note by add each harmonic by the amplitude comparatively with the basic frequency
        #產生諧波,參數：幅度，頻率，8000 ，結束=0.5
        pianonote = pianonote + gen_sin(ampli[startpoint] /50* Amp[i], nomalizedbasicfreq[startpoint] * (i + 1), 8000, 0.125*notestime[w])
        #矢量加法
    #attenuate the note with the time domain feature
    #進行衰減
    for k in range(0,windowsize*notestime[w]):#k:0---4000
        pianomusic[startpoint*windowsize+k]=pianonote[k]*attenuation[k]
        #0--4000 startpoint=0
        #4000-8000   =4
        #8000-12000  =8
        #36*1000---36*1000+4000（40000）4萬
    startpoint=startpoint+notestime[w] #record the start point of the next note
    #startpoint變化規律：0,4,8,12，...32，36

for i in range(0,len(wave_data[0])):
    wave_data[0][i]=pianomusic[i]


#get a wave file
f = wave.open(r"pianomusic.wav", "wb")
#get the channel, sampling width and sampling frequency information
#see details in 2.9 of my report
f.setnchannels(1)
f.setsampwidth(2)
f.setframerate(8000)
f.writeframes(wave_data[0].tostring()) #put the data into the wave file
f.close()

print("STEP 9: please see in figure and listen the pianomusic.wav file")
plt.figure()
plt.subplot(211)
plt.plot(Amp)
plt.title(r'frequency domain harmonic feature')
plt.subplot(212)
plt.plot(attenuation)
plt.title(r'time domain attenuation feature')
plt.figure()
plt.plot(pianomusic)
plt.title(r'STEP 9:reform it into piano')
plt.show()

            
          

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