采点,绘制波形图,显示频谱
import wave
import pyaudio
import numpy
import pylab
#打开WAV文档,文件路径根据需要做修改
wf = wave.open("test.wav", "rb")
#创建PyAudio对象
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=wf.getnchannels(),
rate=wf.getframerate(),
output=True)
nframes = wf.getnframes()
framerate = wf.getframerate()
#读取完整的帧数据到str_data中,这是一个string类型的数据
str_data = wf.readframes(nframes)
wf.close()
#将波形数据转换为数组
# A new 1-D array initialized from raw binary or text data in a string.
wave_data = numpy.fromstring(str_data, dtype=numpy.short)
#将wave_data数组改为2列,行数自动匹配。在修改shape的属性时,需使得数组的总长度不变。
wave_data.shape = -1,2
#将数组转置
wave_data = wave_data.T
#time 也是一个数组,与wave_data[0]或wave_data[1]配对形成系列点坐标
#time = numpy.arange(0,nframes)*(1.0/framerate)
#绘制波形图
#pylab.plot(time, wave_data[0])
#pylab.subplot(212)
#pylab.plot(time, wave_data[1], c="g")
#pylab.xlabel("time (seconds)")
#pylab.show()
#
# 采样点数,修改采样点数和起始位置进行不同位置和长度的音频波形分析
N=44100
start=0 #开始采样位置
df = framerate/(N-1) # 分辨率
freq = [df*n for n in range(0,N)] #N个元素
wave_data2=wave_data[0][start:start+N]
c=numpy.fft.fft(wave_data2)*2/N
#常规显示采样频率一半的频谱
d=int(len(c)/2)
#仅显示频率在4000以下的频谱
while freq[d]>4000:
d-=10
pylab.plot(freq[:d-1],abs(c[:d-1]),'r')
pylab.show()
import os
import argparse
import numpy as np
from scipy.io import wavfile
from hmmlearn import hmm
from python_speech_features import mfcc
# 解析命令行的输入参数
def build_arg_parser():
parser = argparse.ArgumentParser(description='Trains the HMM classifier')
parser.add_argument("--input-folder", dest="input_folder", required=True,
help="Input folder containing the audio files in subfolders")
return parser
# 创建类,处理HMM相关过程
class HMMTrainer(object):
'''用到高斯隐马尔科夫模型
n_components:定义了隐藏状态的个数
cov_type:定义了转移矩阵的协方差类型
n_iter:定义了训练的迭代次数
'''
def __init__(self, model_name='GaussianHMM', n_components=4, cov_type='diag', n_iter=1000):
self.model_name = model_name
self.n_components = n_components
self.cov_type = cov_type
self.n_iter = n_iter
self.models = []
if self.model_name == 'GaussianHMM':
self.model = hmm.GaussianHMM(n_components=self.n_components,
covariance_type=self.cov_type, n_iter=self.n_iter)
else:
raise TypeError('Invalid model type')
# X是二维数组,其中每一行有13个数
def train(self, X):
np.seterr(all='ignore')
self.models.append(self.model.fit(X))
# 对输入数据运行模型
def get_score(self, input_data):
return self.model.score(input_data)
if __name__=='__main__':
# 解析输入参数
args = build_arg_parser().parse_args()
input_folder = args.input_folder
hmm_models = [] # 初始化隐马尔科夫模型的变量
# 解析输入路径
for dirname in os.listdir(input_folder):
# 获取子文件夹名称
subfolder = os.path.join(input_folder, dirname)
if not os.path.isdir(subfolder):
continue
# 子文件夹名称即为该类的标记
# 提取特征
label = subfolder[subfolder.rfind('/') + 1:]
# 初始化变量
X = np.array([])
y_words = []
# 迭代所有音频文件(分别保留一个进行测试)
for filename in [x for x in os.listdir(subfolder) if x.endswith('.wav')][:-1]:
# 读取每个音频文件
filepath = os.path.join(subfolder, filename)
sampling_freq, audio = wavfile.read(filepath)
# 提取MFCC特征
mfcc_features = mfcc(audio, sampling_freq)
# 将MFCC特征添加到X变量
if len(X) == 0:
X = mfcc_features
else:
X = np.append(X, mfcc_features, axis=0)
# 添加标记
y_words.append(label)
print('X.shape =', X.shape)
# 训练并且保存HMM模型
hmm_trainer = HMMTrainer()
hmm_trainer.train(X)
hmm_models.append((hmm_trainer, label))
hmm_trainer = None
# 测试文件
input_files = [
'data/pineapple/pineapple15.wav',
'data/orange/orange15.wav',
'data/apple/apple15.wav',
'data/kiwi/kiwi15.wav'
]
# 为输入数据分类
for input_file in input_files:
# 读取每个音频文件
sampling_freq, audio = wavfile.read(input_file)
# 提取MFCC特征
mfcc_features = mfcc(audio, sampling_freq)
# 定义变量
max_score = None
output_label = None
# 迭代HMM模型并选取得分最高的模型
for item in hmm_models:
hmm_model, label = item
score = hmm_model.get_score(mfcc_features)
if score > max_score:
max_score = score
output_label = label
# 打印结果
print("\nTrue:", input_file[input_file.find('/')+1:input_file.rfind('/')])
print("Predicted:", output_label)
#! /usr/bin/env python
#! -*- coding=utf-8 -*-
#模拟两个正态分布的均值估计
from numpy import *
import numpy as np
import random
import copy
SIGMA = 6
EPS = 0.0001
#生成方差相同,均值不同的样本
def generate_data():
Miu1 = 20
Miu2 = 40
N = 1000
X = mat(zeros((N,1)))
for i in range(N):
temp = random.uniform(0,1)
if(temp > 0.5):
X[i] = temp*SIGMA + Miu1
else:
X[i] = temp*SIGMA + Miu2
return X
#EM算法
def my_EM(X):
k = 2
N = len(X)
Miu = np.random.rand(k,1)
Posterior = mat(zeros((N,2)))
dominator = 0
numerator = 0
#先求后验概率
for iter in range(1000):
for i in range(N):
dominator = 0
for j in range(k):
dominator = dominator + np.exp(-1.0/(2.0*SIGMA**2) * (X[i] - Miu[j])**2)
#print dominator,-1/(2*SIGMA**2) * (X[i] - Miu[j])**2,2*SIGMA**2,(X[i] - Miu[j])**2
#return
for j in range(k):
numerator = np.exp(-1.0/(2.0*SIGMA**2) * (X[i] - Miu[j])**2)
Posterior[i,j] = numerator/dominator
oldMiu = copy.deepcopy(Miu)
#最大化
for j in range(k):
numerator = 0
dominator = 0
for i in range(N):
numerator = numerator + Posterior[i,j] * X[i]
dominator = dominator + Posterior[i,j]
Miu[j] = numerator/dominator
print ((abs(Miu - oldMiu)).sum() )
#print '\n'
if (abs(Miu - oldMiu)).sum() < EPS:
print (Miu,iter)
break
if __name__ == '__main__':
X = generate_data()
my_EM(X)