一、项目需求
训练集数据为六类音乐([“classical”, “jazz”, “country”, “pop”, “rock”, “metal”])，格式为.wav，每类音乐都有100首
音乐分类器项目，主要运用到了傅里叶变换函数
很多东西越在高维空间处理起来就会变得越是简单
例如：书本上的文字是一维，漫画图像是二维，视频是三维(加上了时间维度)，你喜欢看书还是看图画书还是看电影？
很显然，视频更容易让人们所接受
一条直线，你从正面看是一条直线，当你从侧面看时，则变成了一个点，这就是观察方向的不同导致的结果不同，但是有影响吗？这根直线还是这根直线，没有变，只不过观察方向角度变了而已。
音乐是有多个频率所构成的，傅里叶公式可以简单的理解为从另一个角度进行观察音乐频率
正常的我们是通过前方(时间维度)进行观察聆听音乐的，而傅里叶则是从右侧(频域)进行观察的
大家都知道，任何一个连续函数都可以用正弦函数叠加，故音乐则可以理解为多个正弦函数的叠加
当你从傅里叶的角度(右侧)进行观察时，就会发现实则是多个峰或者说是多条直线而已，问题瞬间变得简单了
二、数据集
这里的音乐使用的都是单声道的音乐(.wav)，通过傅里叶变换将音频进行转化为频谱(.fft.npy)
若其他同学手边有数据也可以自己进行转换
可以参考该篇博文：.wav音乐文件转换为.fft.npy频谱格式文件
若不想自己动手转换，可以直接使用这个数据集：.fft.npy格式音乐经过傅里叶变换得到的频谱数据集
训练集：
测试集：
三、完整代码
需要修改的地方：
rad = "G:/PyCharm/workspace/machine_learning/trainset/"+g+"."+str(n).zfill(5)+ ".fft"+".npy"训练集路径
wavfile.read("G:/PyCharm/workspace/machine_learning/trainset/sample/heibao-wudizirong-remix.wav")测试集路径
# coding:utf-8
import 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随机变量的意思
# 在模拟X的时候使用了两个正态分布,分别制定各自的均值,方差,生成40个点
X = np.hstack((norm.rvs(loc=2, size=n, scale=2), norm.rvs(loc=8, size=n, scale=3)))
# zeros使得数据点生成40个0,ones使得数据点生成40个1
y = np.hstack((np.zeros(n),np.ones(n)))
# 创建一个 10 * 4 点（point）的图，并设置分辨率为 80
plt.figure(figsize=(10, 4),dpi=80)
# 设置横轴的上下限
plt.xlim((-5, 20))
# scatter散点图
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的区间内找10个数
xs=np.linspace(-5,15,10)
#---linear regression----------
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
# reshape重新把array变成了80行1列二维数组,符合机器学习多维线性回归格式
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变成了80行1列二维数组,符合机器学习多维线性回归格式
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))
# 创建一个一行两列子图的图像中第一个图
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')
# 创建一个一行两列子图的图像中第二个图
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处理音乐数据,音乐数据训练样本的获得和使用快速傅里叶变换（FFT）预处理的方法需要事先准备好
1. 把训练集扩大到每类100个首歌而不是之前的10首歌,类别仍然是六类:jazz,classical,country, pop, rock, metal
2. 同时使用logistic回归和KNN作为分类器
3. 引入一些评价的标准来比较Logistic和KNN在测试集上的表现
"""
# 准备音乐数据
# def create_fft(g, n):
# rad = "d:/genres/"+g+"/converted/"+g+"."+str(n).zfill(5)+".au.wav"#音乐文件的路径，这里的音乐文件都是.wav格式
# sample_rate, X = wavfile.read(rad)#sample_rate采样率；X为音乐文件本身
# fft_features = abs(fft(X)[:1000])#对音乐文件本身进行fft快速傅里叶变化，取前1000赫兹数据，进行取绝对值，得到fft_features傅里叶变换的特征
# sad = "d:/trainset/"+g+"."+str(n).zfill(5) + ".fft"#将特征存储到这个路径下
# np.save(sad, fft_features)#存储特征，存储的是.fft格式，但是最终生成的是.fft.npy格式，这是numpy自动生成的
#
# # -------create fft--------------
#
#
# genre_list = ["classical", "jazz", "country", "pop", "rock", "metal"]
# for g in genre_list:
# for n in range(100):
# create_fft(g, n)
# 加载训练集数据,分割训练集以及测试集,进行分类器的训练
# 构造训练集！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
# -------read fft--------------
genre_list = ["classical", "jazz", "country", "pop", "rock", "metal"]
X = []#矩阵
Y = []#标签
for g in genre_list:
for n in range(100):#六类音乐，每类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)#把列表转化为array数组类型
Y = np.array(Y)
"""
# 首先我们要将原始数据分为训练集和测试集，这里是随机抽样80%做测试集，剩下20%做训练集
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])
"""
# 接下来，我们使用sklearn，来构造和训练我们的两种分类器
# ------train logistic classifier--------------
from sklearn.linear_model import LogisticRegression
#multi_class='ovr'表示使用逻辑回归的多分类，若为multinomial则使用softmax进行多分类；solver='sag'使用随机梯度下降法，若不传则默认使用liblinear；max_iter=10000最大迭代次数
model = LogisticRegression()#创建逻辑回归对象
model.fit(X, Y)#传入数据进行训练模型，这里的model是在内存里面
# predictYlogistic=map(lambda x:logclf.predict(x)[0],testX)
# 可以采用Python内建的持久性模型 pickle 来保存scikit的模型，下面的代码是将model存到硬盘里面
# 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)
# 将predictYlogistic以及predictYknn与testY对比，我们就可以知道两者的判定正确率
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 = givenAX
ax.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")
"""
输出结果如下：
其中44100为采样率
"""
44100 [2.62963968e+08 4.46200714e+06 5.15395385e+06 4.43376757e+06
3.74566845e+06 4.70186729e+06 5.33935836e+06 4.61999477e+06
4.15225785e+06 3.30594588e+06 4.31103082e+06 5.05558675e+06
5.32297672e+06 4.85506026e+06 5.19126496e+06 4.14585787e+06
3.62846270e+06 4.75147337e+06 3.91082781e+06 4.62259775e+06
4.48238357e+06 3.23787122e+06 2.22014030e+06 3.78805307e+06
2.14269906e+06 3.13617469e+06 3.12430918e+06 4.46836068e+06
2.74392608e+06 3.78402588e+06 1.87577534e+06 3.49203803e+06
2.33558316e+06 3.81553507e+06 2.63688151e+06 3.02667708e+06
2.04674785e+06 2.22072034e+06 1.91995398e+06 1.98927890e+06
1.68224240e+06 1.35139077e+06 2.30549645e+06 6.03046897e+05
1.31438823e+06 1.95087823e+06 8.49833030e+05 1.19171948e+06
1.23251661e+06 1.89390106e+06 5.80352061e+05 1.50388290e+06
1.25938435e+06 1.02978969e+06 2.36614610e+05 3.01560843e+05
1.27662352e+06 1.49221129e+06 5.17213388e+05 8.00948818e+05
7.65561994e+05 3.62419277e+05 1.50979436e+06 3.85068213e+05
6.41942889e+05 3.61144502e+05 6.85268116e+05 1.00144583e+06
6.46261080e+05 1.40845476e+06 6.62866166e+05 6.91106024e+05
1.23208363e+06 1.36027432e+06 3.62846259e+05 5.72218147e+05
7.75993152e+05 9.14515445e+05 1.18571572e+06 9.02475526e+05
4.98999881e+05 1.74914232e+06 3.94735421e+05 1.22194083e+06
9.44511346e+05 5.64374132e+05 1.76153158e+06 1.92086536e+06
1.23147054e+06 3.62420572e+05 5.19808732e+05 1.34346298e+06
7.21219553e+05 8.88950439e+05 1.75325706e+06 2.29355413e+06
1.08391025e+06 9.30282476e+05 1.10235851e+06 3.67805257e+05
5.77443645e+05 5.94086277e+05 1.19729395e+06 5.34697818e+05
3.88725959e+05 7.87438862e+05 1.77019327e+06 1.66520041e+06
2.07569988e+06 7.36173308e+05 6.56954650e+05 1.61943917e+06
8.67054883e+05 1.26014326e+06 1.61921808e+06 1.54533344e+06
7.07774874e+05 1.62786750e+05 2.97020086e+05 1.13388210e+06
5.63030498e+05 9.58680710e+05 1.16377079e+06 9.77142004e+05
8.99557347e+05 4.93741261e+05 1.99306708e+05 1.20241539e+06
7.47989489e+04 1.67983186e+06 6.79762302e+05 8.65937699e+05
5.50519377e+05 1.72907596e+06 2.93786505e+05 7.75062173e+05
9.68810309e+05 1.43654854e+06 8.08623022e+05 2.59287374e+05
4.57334088e+05 9.85646332e+05 1.38921416e+06 1.17264490e+06
7.41666163e+05 2.05204503e+06 8.05602385e+05 7.40724129e+05
9.05423650e+05 4.47600257e+05 9.70026356e+05 1.19707145e+06
8.66600040e+05 9.09215043e+05 6.38983412e+05 6.24539950e+05
2.30489745e+05 9.49711853e+05 1.62124067e+06 1.33000213e+06
7.49378742e+05 4.94629349e+05 2.53593993e+05 8.26687520e+05
7.83442269e+05 1.10375179e+06 7.42007345e+05 5.10356938e+05
4.41047858e+05 8.81833105e+05 2.42540213e+06 3.30401795e+05
8.80062590e+05 6.49905701e+05 3.47149964e+05 1.05602066e+06
1.18980862e+06 1.96891139e+05 1.14120246e+06 8.02951614e+05
6.00111367e+05 6.69160363e+05 1.89491761e+05 5.95679660e+05
1.38204166e+06 1.21312932e+06 1.00683336e+06 1.71907045e+06
1.47929539e+06 1.05542669e+06 2.36641708e+06 9.33510819e+05
7.48075310e+05 1.77764553e+06 4.21246379e+05 7.90733672e+05
1.39589725e+06 7.09737413e+05 7.30502934e+05 1.25361964e+06
6.19123157e+05 6.88892351e+05 6.26028154e+05 8.54795006e+05
7.06965041e+05 1.21987749e+06 7.52060331e+05 4.71848433e+05
2.36098779e+05 8.47966863e+05 4.01129984e+05 1.27542408e+06
7.14220047e+05 5.18027090e+05 5.58925060e+05 2.79974210e+05
7.62875042e+05 1.84079472e+06 1.42550029e+06 1.14811203e+06
1.30128360e+06 1.24869130e+06 3.52352404e+05 4.21421728e+05
1.18173485e+06 5.10136084e+05 3.75560127e+05 4.79102195e+05
1.10333151e+06 2.05555702e+06 7.23702249e+05 3.61178799e+05
7.04851219e+05 1.44012108e+06 9.26402727e+05 2.02821056e+05
4.45325652e+05 7.90522874e+05 8.26436685e+05 1.17563229e+06
8.43867568e+05 4.01048078e+05 9.26085978e+05 6.44995771e+05
3.53685137e+05 8.34366832e+05 1.23386512e+06 7.02375781e+05
4.58931591e+05 8.43526489e+05 1.10676720e+06 7.68521715e+05
1.62269410e+06 1.58265455e+06 1.03200640e+06 4.64795983e+05
9.90089422e+05 5.24337673e+05 3.66658840e+05 6.55805698e+05
5.70752207e+05 2.76033608e+05 3.23297345e+05 1.33078141e+06
6.16117851e+05 3.63376716e+05 1.22034305e+06 1.87348103e+06
8.18691165e+05 2.43489854e+05 1.06339848e+06 1.38161299e+05
7.08259014e+05 1.32420185e+06 3.73634708e+05 4.74188119e+05
8.56522386e+05 1.18141641e+06 1.52211708e+06 4.07008939e+05
8.35340660e+05 6.40881238e+05 1.09076459e+06 1.21963072e+06
7.56369038e+05 1.14117511e+06 1.39389976e+06 5.10490203e+05
7.00735069e+05 2.37107675e+05 8.94992427e+05 1.53159359e+06
5.24539638e+05 7.88949376e+04 8.78275390e+05 1.68422430e+06
8.09255981e+05 4.57182807e+05 1.17241872e+06 2.09394245e+05
9.77952748e+04 8.97739813e+05 1.08742141e+06 9.69384074e+05
9.81838586e+05 5.07041429e+05 1.15578207e+06 3.49371827e+05
6.20229249e+05 3.60935229e+05 1.03026311e+06 3.46570750e+05
1.08685762e+06 1.66151632e+06 9.47736790e+05 4.26436467e+05
1.13526476e+06 5.84625497e+05 1.51928634e+06 9.70060181e+05
1.62523526e+06 6.11541462e+05 4.29298422e+05 4.61724329e+05
5.66555319e+05 1.29364175e+06 1.01953071e+06 1.94211951e+06
2.72120805e+05 1.14321213e+06 5.67287402e+05 1.94838376e+06
8.23364882e+05 1.62185476e+06 1.12559716e+06 4.55413724e+05
3.94762550e+05 9.96679018e+05 8.64137068e+05 9.73976199e+05
5.00463157e+05 5.05326117e+05 7.18463504e+05 3.00032365e+05
1.29926845e+06 9.28358383e+05 4.51525493e+05 4.65797885e+05
6.70108099e+05 1.00455574e+06 1.22544843e+05 7.84409036e+05
2.52051242e+06 1.13223858e+06 8.64798855e+05 7.61259423e+05
9.48682621e+05 5.33148975e+05 3.99035609e+05 1.47581282e+06
2.39172803e+06 6.02291705e+05 9.12335511e+05 1.10007913e+06
3.88451743e+05 3.18750364e+05 1.12384444e+06 5.00540271e+05
1.60319399e+05 7.27587480e+05 6.17185073e+05 1.35890728e+06
1.56238108e+06 9.65851067e+05 1.07019478e+06 1.63850864e+06
6.47843129e+05 2.65218848e+05 1.17738892e+06 1.19611403e+06
2.75522201e+05 1.34222981e+06 2.75771621e+05 1.98303864e+05
7.81263330e+05 3.26290246e+05 1.74968242e+06 7.61635689e+05
7.82203438e+05 4.37269114e+05 4.94924240e+05 4.04790850e+05
6.06235962e+05 1.14968590e+06 9.01138935e+05 3.87643407e+05
1.25101978e+06 1.25419381e+06 1.35912365e+06 1.10417253e+06
2.56006176e+05 1.68242848e+05 6.92147285e+05 5.62794655e+05
4.93718498e+05 5.32081292e+05 1.27889173e+06 1.07027869e+06
1.10223160e+06 1.21005716e+06 8.85081307e+05 9.54022330e+05
1.09599439e+06 1.00597502e+06 1.32263178e+06 9.81496867e+05
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4
rock
"""
