一、項目需求

訓練集數據為六類音樂([“classical”, “jazz”, “country”, “pop”, “rock”, “metal”])，格式為.wav，每類音樂都有100首
音樂分類器項目，主要運用到了傅里葉變換函數
很多東西越在高維空間處理起來就會變得越是簡單
例如：書本上的文字是一維，漫畫圖像是二維，視頻是三維(加上了時間維度)，你喜歡看書還是看圖畫書還是看電影？
很顯然，視頻更容易讓人們所接受

一條直線，你從正面看是一條直線，當你從側面看時，則變成了一個點，這就是觀察方向的不同導致的結果不同，但是有影響嗎？這根直線還是這根直線，沒有變，只不過觀察方向角度變了而已。
音樂是有多個頻率所構成的，傅里葉公式可以簡單的理解為從另一個角度進行觀察音樂頻率
正常的我們是通過前方(時間維度)進行觀察聆聽音樂的，而傅里葉則是從右側(頻域)進行觀察的
大家都知道，任何一個連續函數都可以用正弦函數疊加，故音樂則可以理解為多個正弦函數的疊加
當你從傅里葉的角度(右側)進行觀察時，就會發現實則是多個峰或者說是多條直線而已，問題瞬間變得簡單了

二、數據集

這里的音樂使用的都是單聲道的音樂(.wav)，通過傅里葉變換將音頻進行轉化為頻譜(.fft.npy)
若其他同學手邊有數據也可以自己進行轉換
可以參考該篇博文：.wav音樂文件轉換為.fft.npy頻譜格式文件
若不想自己動手轉換，可以直接使用這個數據集：.fft.npy格式音樂經過傅里葉變換得到的頻譜數據集

訓練集：
測試集：

三、完整代碼

需要修改的地方：
rad = "G:/PyCharm/workspace/machine_learning/trainset/"+g+"."+str(n).zfill(5)+ ".fft"+".npy"訓練集路徑
wavfile.read("G:/PyCharm/workspace/machine_learning/trainset/sample/heibao-wudizirong-remix.wav")測試集路徑

# coding:utf-8import numpy as np
from sklearn import linear_model, datasets
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.fftpack import fft
from scipy.io import wavfile"""
n = 40
# hstack使得十足拼接
# rvs是Random Variates隨機變量的意思
# 在模擬X的時候使用了兩個正態分布,分別制定各自的均值,方差,生成40個點
X = np.hstack((norm.rvs(loc=2, size=n, scale=2), norm.rvs(loc=8, size=n, scale=3)))
# zeros使得數據點生成40個0,ones使得數據點生成40個1
y = np.hstack((np.zeros(n),np.ones(n)))
# 創建一個 10 * 4 點（point）的圖，并設置分辨率為 80
plt.figure(figsize=(10, 4),dpi=80)
# 設置橫軸的上下限
plt.xlim((-5, 20))
# scatter散點圖
plt.scatter(X, y, c=y)
plt.xlabel("feature value")
plt.ylabel("class")
plt.grid(True, linestyle='-', color='0.75')
plt.savefig("D:/workspace/scikit-learn/logistic_classify.png", bbox_inches="tight")
""""""
# linspace是在-5到15的區間內找10個數
xs=np.linspace(-5,15,10)#---linear regression----------
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
# reshape重新把array變成了80行1列二維數組,符合機器學習多維線性回歸格式
clf.fit(X.reshape(n * 2, 1), y)
def lin_model(clf, X):return clf.intercept_ + clf.coef_ * X#---logistic regression--------
from sklearn.linear_model import LogisticRegression
logclf = LogisticRegression()
# reshape重新把array變成了80行1列二維數組,符合機器學習多維線性回歸格式
logclf.fit(X.reshape(n * 2, 1), y)
def lr_model(clf, X):return 1.0 / (1.0 + np.exp(-(clf.intercept_ + clf.coef_ * X)))#----plot---------------------------    
plt.figure(figsize=(10, 5))
# 創建一個一行兩列子圖的圖像中第一個圖
plt.subplot(1, 2, 1)
plt.scatter(X, y, c=y)
plt.plot(X, lin_model(clf, X),"o",color="orange")
plt.plot(xs, lin_model(clf, xs),"-",color="green")
plt.xlabel("feature value")
plt.ylabel("class")
plt.title("linear fit")
plt.grid(True, linestyle='-', color='0.75')
# 創建一個一行兩列子圖的圖像中第二個圖
plt.subplot(1, 2, 2)
plt.scatter(X, y, c=y)
plt.plot(X, lr_model(logclf, X).ravel(),"o",color="c")
plt.plot(xs, lr_model(logclf, xs).ravel(),"-",color="green")
plt.xlabel("feature value")
plt.ylabel("class")
plt.title("logistic fit")
plt.grid(True, linestyle='-', color='0.75')plt.tight_layout(pad=0.4, w_pad=0, h_pad=1.0)     
plt.savefig("D:/workspace/scikit-learn/logistic_classify2.png", bbox_inches="tight")
""""""
使用logistic regression處理音樂數據,音樂數據訓練樣本的獲得和使用快速傅里葉變換（FFT）預處理的方法需要事先準備好
1. 把訓練集擴大到每類100個首歌而不是之前的10首歌,類別仍然是六類:jazz,classical,country, pop, rock, metal
2. 同時使用logistic回歸和KNN作為分類器
3. 引入一些評價的標準來比較Logistic和KNN在測試集上的表現 
"""# 準備音樂數據
# def create_fft(g, n):
#     rad = "d:/genres/"+g+"/converted/"+g+"."+str(n).zfill(5)+".au.wav"#音樂文件的路徑，這里的音樂文件都是.wav格式
#     sample_rate, X = wavfile.read(rad)#sample_rate采樣率；X為音樂文件本身
#     fft_features = abs(fft(X)[:1000])#對音樂文件本身進行fft快速傅里葉變化，取前1000赫茲數據，進行取絕對值，得到fft_features傅里葉變換的特征
#     sad = "d:/trainset/"+g+"."+str(n).zfill(5) + ".fft"#將特征存儲到這個路徑下
#     np.save(sad, fft_features)#存儲特征，存儲的是.fft格式，但是最終生成的是.fft.npy格式，這是numpy自動生成的
#
# # -------create fft--------------
#
#
# genre_list = ["classical", "jazz", "country", "pop", "rock", "metal"]
# for g in genre_list:
#     for n in range(100):
#         create_fft(g, n)# 加載訓練集數據,分割訓練集以及測試集,進行分類器的訓練
# 構造訓練集！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
# -------read fft--------------
genre_list = ["classical", "jazz", "country", "pop", "rock", "metal"]
X = []#矩陣
Y = []#標簽
for g in genre_list:for n in range(100):#六類音樂，每類100首rad = "G:/PyCharm/workspace/machine_learning/trainset/"+g+"."+str(n).zfill(5)+ ".fft"+".npy"fft_features = np.load(rad)X.append(fft_features)Y.append(genre_list.index(g))X = np.array(X)#把列表轉化為array數組類型
Y = np.array(Y)
"""
# 首先我們要將原始數據分為訓練集和測試集，這里是隨機抽樣80%做測試集，剩下20%做訓練集 
import random
randomIndex=random.sample(range(len(Y)),int(len(Y)*8/10))
trainX=[];trainY=[];testX=[];testY=[]
for i in range(len(Y)):if i in randomIndex:trainX.append(X[i])trainY.append(Y[i])else:testX.append(X[i])testY.append(Y[i])
"""# 接下來，我們使用sklearn，來構造和訓練我們的兩種分類器 
# ------train logistic classifier--------------
from sklearn.linear_model import LogisticRegression
#multi_class='ovr'表示使用邏輯回歸的多分類，若為multinomial則使用softmax進行多分類；solver='sag'使用隨機梯度下降法，若不傳則默認使用liblinear；max_iter=10000最大迭代次數
model = LogisticRegression()#創建邏輯回歸對象
model.fit(X, Y)#傳入數據進行訓練模型，這里的model是在內存里面
# predictYlogistic=map(lambda x:logclf.predict(x)[0],testX)# 可以采用Python內建的持久性模型 pickle 來保存scikit的模型，下面的代碼是將model存到硬盤里面
# import pickle
# s = pickle.dumps(model)
# clf2 = pickle.loads(s)
# clf2.predict(X[0])"""
#----train knn classifier-----------------------
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(trainX) 
predictYknn=map(lambda x:trainY[neigh.kneighbors(x,return_distance=False)[0][0]],testX)# 將predictYlogistic以及predictYknn與testY對比，我們就可以知道兩者的判定正確率 
a = np.array(predictYlogistic)-np.array(testY)
print a, np.count_nonzero(a), len(a)
accuracyLogistic = 1-np.count_nonzero(a)/(len(a)*1.0)
b = np.array(predictYknn)-np.array(testY)
print b, np.count_nonzero(b), len(b)
accuracyKNN = 1-np.count_nonzero(b)/(len(b)*1.0)print "%f" % (accuracyLogistic)
print "%f" % (accuracyKNN)
"""print('Starting read wavfile...')
# prepare test data-------------------
# sample_rate, test = wavfile.read("d:/trainset/sample/outfile.wav")
sample_rate, test = wavfile.read("G:/PyCharm/workspace/machine_learning/trainset/sample/heibao-wudizirong-remix.wav")
# sample_rate, test = wavfile.read("d:/genres/metal/converted/metal.00080.au.wav")
testdata_fft_features = abs(fft(test))[:1000]
print(sample_rate, testdata_fft_features, len(testdata_fft_features))
type_index = model.predict([testdata_fft_features])[0]
print(type_index)
print(genre_list[type_index])"""
from sklearn.metrics import confusion_matrix
cmlogistic = confusion_matrix(testY, predictYlogistic)
cmknn = confusion_matrix(testY, predictYknn)def plotCM(cm,title,colorbarOn,givenAX):ncm=cm/cm.max()plt.matshow(ncm, fignum=False, cmap='Blues', vmin=0, vmax=2.0)if givenAX=="":ax=plt.axes()else:ax = givenAXax.set_xticks(range(len(genre_list)))ax.set_xticklabels(genre_list)ax.xaxis.set_ticks_position("bottom")ax.set_yticks(range(len(genre_list)))ax.set_yticklabels(genre_list)plt.title(title,size=12)if colorbarOn=="on":plt.colorbar()plt.xlabel('Predicted class')plt.ylabel('True class')for i in range(cm.shape[0]):for j in range(cm.shape[1]):plt.text(i,j,cm[i,j],size=15)plt.figure(figsize=(10, 5))  
fig1=plt.subplot(1, 2, 1)          
plotCM(cmlogistic,"confusion matrix: FFT based logistic classifier","off",fig1.axes)   
fig2=plt.subplot(1, 2, 2)     
plotCM(cmknn,"confusion matrix: FFT based KNN classifier","off",fig2.axes) 
plt.tight_layout(pad=0.4, w_pad=0, h_pad=1.0)     plt.savefig("d:/confusion_matrix.png", bbox_inches="tight")
"""

輸出結果如下：
其中44100為采樣率

"""
44100 [2.62963968e+08 4.46200714e+06 5.15395385e+06 4.43376757e+063.74566845e+06 4.70186729e+06 5.33935836e+06 4.61999477e+064.15225785e+06 3.30594588e+06 4.31103082e+06 5.05558675e+065.32297672e+06 4.85506026e+06 5.19126496e+06 4.14585787e+063.62846270e+06 4.75147337e+06 3.91082781e+06 4.62259775e+064.48238357e+06 3.23787122e+06 2.22014030e+06 3.78805307e+062.14269906e+06 3.13617469e+06 3.12430918e+06 4.46836068e+062.74392608e+06 3.78402588e+06 1.87577534e+06 3.49203803e+062.33558316e+06 3.81553507e+06 2.63688151e+06 3.02667708e+062.04674785e+06 2.22072034e+06 1.91995398e+06 1.98927890e+061.68224240e+06 1.35139077e+06 2.30549645e+06 6.03046897e+051.31438823e+06 1.95087823e+06 8.49833030e+05 1.19171948e+061.23251661e+06 1.89390106e+06 5.80352061e+05 1.50388290e+061.25938435e+06 1.02978969e+06 2.36614610e+05 3.01560843e+051.27662352e+06 1.49221129e+06 5.17213388e+05 8.00948818e+057.65561994e+05 3.62419277e+05 1.50979436e+06 3.85068213e+056.41942889e+05 3.61144502e+05 6.85268116e+05 1.00144583e+066.46261080e+05 1.40845476e+06 6.62866166e+05 6.91106024e+051.23208363e+06 1.36027432e+06 3.62846259e+05 5.72218147e+057.75993152e+05 9.14515445e+05 1.18571572e+06 9.02475526e+054.98999881e+05 1.74914232e+06 3.94735421e+05 1.22194083e+069.44511346e+05 5.64374132e+05 1.76153158e+06 1.92086536e+061.23147054e+06 3.62420572e+05 5.19808732e+05 1.34346298e+067.21219553e+05 8.88950439e+05 1.75325706e+06 2.29355413e+061.08391025e+06 9.30282476e+05 1.10235851e+06 3.67805257e+055.77443645e+05 5.94086277e+05 1.19729395e+06 5.34697818e+053.88725959e+05 7.87438862e+05 1.77019327e+06 1.66520041e+062.07569988e+06 7.36173308e+05 6.56954650e+05 1.61943917e+068.67054883e+05 1.26014326e+06 1.61921808e+06 1.54533344e+067.07774874e+05 1.62786750e+05 2.97020086e+05 1.13388210e+065.63030498e+05 9.58680710e+05 1.16377079e+06 9.77142004e+058.99557347e+05 4.93741261e+05 1.99306708e+05 1.20241539e+067.47989489e+04 1.67983186e+06 6.79762302e+05 8.65937699e+055.50519377e+05 1.72907596e+06 2.93786505e+05 7.75062173e+059.68810309e+05 1.43654854e+06 8.08623022e+05 2.59287374e+054.57334088e+05 9.85646332e+05 1.38921416e+06 1.17264490e+067.41666163e+05 2.05204503e+06 8.05602385e+05 7.40724129e+059.05423650e+05 4.47600257e+05 9.70026356e+05 1.19707145e+068.66600040e+05 9.09215043e+05 6.38983412e+05 6.24539950e+052.30489745e+05 9.49711853e+05 1.62124067e+06 1.33000213e+067.49378742e+05 4.94629349e+05 2.53593993e+05 8.26687520e+057.83442269e+05 1.10375179e+06 7.42007345e+05 5.10356938e+054.41047858e+05 8.81833105e+05 2.42540213e+06 3.30401795e+058.80062590e+05 6.49905701e+05 3.47149964e+05 1.05602066e+061.18980862e+06 1.96891139e+05 1.14120246e+06 8.02951614e+056.00111367e+05 6.69160363e+05 1.89491761e+05 5.95679660e+051.38204166e+06 1.21312932e+06 1.00683336e+06 1.71907045e+061.47929539e+06 1.05542669e+06 2.36641708e+06 9.33510819e+057.48075310e+05 1.77764553e+06 4.21246379e+05 7.90733672e+051.39589725e+06 7.09737413e+05 7.30502934e+05 1.25361964e+066.19123157e+05 6.88892351e+05 6.26028154e+05 8.54795006e+057.06965041e+05 1.21987749e+06 7.52060331e+05 4.71848433e+052.36098779e+05 8.47966863e+05 4.01129984e+05 1.27542408e+067.14220047e+05 5.18027090e+05 5.58925060e+05 2.79974210e+057.62875042e+05 1.84079472e+06 1.42550029e+06 1.14811203e+061.30128360e+06 1.24869130e+06 3.52352404e+05 4.21421728e+051.18173485e+06 5.10136084e+05 3.75560127e+05 4.79102195e+051.10333151e+06 2.05555702e+06 7.23702249e+05 3.61178799e+057.04851219e+05 1.44012108e+06 9.26402727e+05 2.02821056e+054.45325652e+05 7.90522874e+05 8.26436685e+05 1.17563229e+068.43867568e+05 4.01048078e+05 9.26085978e+05 6.44995771e+053.53685137e+05 8.34366832e+05 1.23386512e+06 7.02375781e+054.58931591e+05 8.43526489e+05 1.10676720e+06 7.68521715e+051.62269410e+06 1.58265455e+06 1.03200640e+06 4.64795983e+059.90089422e+05 5.24337673e+05 3.66658840e+05 6.55805698e+055.70752207e+05 2.76033608e+05 3.23297345e+05 1.33078141e+066.16117851e+05 3.63376716e+05 1.22034305e+06 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4
rock
"""