4

我正在尝试将音频信号从语音分类到情绪。为此，我正在提取音频信号的 MFCC 特征并将它们输入一个简单的神经网络（使用 PyBrain 的 BackpropTrainer 训练的 FeedForwardNetwork）。不幸的是，结果非常糟糕。结果，从 5 个类中，网络似乎几乎总是提出相同的类。

我有 5 类情绪和大约 7000 个标记的音频文件，我将它们划分为每个类的 80% 用于训练网络，20% 用于测试网络。

这个想法是使用小窗口并从中提取 MFCC 特征以生成大量训练示例。在评估中，来自一个文件的所有窗口都被评估，多数票决定预测标签。

Training examples per class: 
{0: 81310, 1: 60809, 2: 58262, 3: 105907, 4: 73182}

Example of scaled MFCC features:
[ -6.03465056e-01   8.28665733e-01  -7.25728303e-01   2.88611116e-05
1.18677218e-02  -1.65316583e-01   5.67322809e-01  -4.92335095e-01   
3.29816126e-01  -2.52946780e-01  -2.26147779e-01   5.27210979e-01   
-7.36851560e-01]

Layers________________________:  13 20 5 (also tried 13 50 5 and 13 100 5)
Learning Rate_________________:  0.01 (also tried 0.1 and 0.3)
Training epochs_______________:  10  (error rate does not improve at all during training)

Truth table on test set:
[[   0.    4.    0.  239.   99.]
 [   0.   41.    0.  157.   23.]
 [   0.   18.    0.  173.   18.]
 [   0.   12.    0.  299.   59.]
 [   0.    0.    0.   85.  132.]]

Success rate overall [%]:  34.7314201619
Success rate Class 0 [%]:  0.0
Success rate Class 1 [%]:  18.5520361991
Success rate Class 2 [%]:  0.0
Success rate Class 3 [%]:  80.8108108108
Success rate Class 4 [%]:  60.8294930876

好的，现在，您可以看到结果在类中的分布非常糟糕。0 类和 2 类永远无法预测。我认为，这暗示我的网络或更可能是我的数据存在问题。

我可以在这里发布很多代码，但我认为在下图中显示我为获得 MFCC 功能而采取的所有步骤更有意义。请注意，我使用整个信号而不加窗只是为了说明。这看起来好吗？MFCC 值非常大，不应该更小吗？（我在将它们输入网络之前将它们缩小，然后使用 minmaxscaler 将所有数据扩展到 [-2,2]，也尝试了 [0,1]）

从信号到 MFCC 的步骤

这是我用于 Melfilter 库的代码，我直接在离散余弦变换之前应用它以提取 MFCC 特征（我从这里得到它：stackoverflow）：

def freqToMel(freq):
  '''
  Calculate the Mel frequency for a given frequency 
  '''
  return 1127.01048 * math.log(1 + freq / 700.0)

def melToFreq(mel):
  '''
  Calculate the frequency for a given Mel frequency 
  '''
  return 700 * (math.exp(freq / 1127.01048 - 1))

def melFilterBank(blockSize):
  numBands = int(mfccFeatures)
  maxMel = int(freqToMel(maxHz))
  minMel = int(freqToMel(minHz))

  # Create a matrix for triangular filters, one row per filter
  filterMatrix = numpy.zeros((numBands, blockSize))

  melRange = numpy.array(xrange(numBands + 2))

  melCenterFilters = melRange * (maxMel - minMel) / (numBands + 1) + minMel

  # each array index represent the center of each triangular filter
  aux = numpy.log(1 + 1000.0 / 700.0) / 1000.0
  aux = (numpy.exp(melCenterFilters * aux) - 1) / 22050
  aux = 0.5 + 700 * blockSize * aux
  aux = numpy.floor(aux)  # Arredonda pra baixo
  centerIndex = numpy.array(aux, int)  # Get int values

  for i in xrange(numBands):
    start, centre, end = centerIndex[i:i + 3]
    k1 = numpy.float32(centre - start)
    k2 = numpy.float32(end - centre)
    up = (numpy.array(xrange(start, centre)) - start) / k1
    down = (end - numpy.array(xrange(centre, end))) / k2

    filterMatrix[i][start:centre] = up
    filterMatrix[i][centre:end] = down

  return filterMatrix.transpose()

我该怎么做才能获得更好的预测结果？

4

1 回答 1

3

在这里，我制作了一个从语音中识别性别的例子。我在这个例子中使用了Hyke-dataset 1。这只是一个快速制作的例子。如果一个人想做严肃的性别识别，也许可以做得更好。但总的来说错误率会降低：

Build up data...
Train network...
Number of training patterns:  94956
Number of test patterns:      31651
Input and output dimensions:  13 2
Train network...
epoch:    0   train error: 62.24%   test error: 61.84%
epoch:    1   train error: 34.11%   test error: 34.25%
epoch:    2   train error: 31.11%   test error: 31.20%
epoch:    3   train error: 30.34%   test error: 30.22%
epoch:    4   train error: 30.76%   test error: 30.75%
epoch:    5   train error: 30.65%   test error: 30.72%
epoch:    6   train error: 30.81%   test error: 30.79%
epoch:    7   train error: 29.38%   test error: 29.45%
epoch:    8   train error: 31.92%   test error: 31.92%
epoch:    9   train error: 29.14%   test error: 29.23%

我使用了scikits.talkbox的 MFCC 实现。也许下面的代码可以帮助你。（性别识别肯定比情绪检测容易得多……也许你需要更多不同的功能。）

import glob

from scipy.io.wavfile import read as wavread
from scikits.talkbox.features import mfcc

from pybrain.datasets            import ClassificationDataSet
from pybrain.utilities           import percentError
from pybrain.tools.shortcuts     import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.structure.modules   import SoftmaxLayer

def report_error(trainer, trndata, tstdata):
    trnresult = percentError(trainer.testOnClassData(), trndata['class'])
    tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
    print "epoch: %4d" % trainer.totalepochs, "  train error: %5.2f%%" % trnresult, "  test error: %5.2f%%" % tstresult  

def main(auido_path, coeffs=13):
    dataset = ClassificationDataSet(coeffs, 1, nb_classes=2, class_labels=['male', 'female'])
    male_files = glob.glob("%s/male_audio/*/*_1.wav" % auido_path)
    female_files = glob.glob("%s/female_audio/*/*_1.wav" % auido_path)

    print "Build up data..."
    for sex, files in enumerate([male_files, female_files]):
        for f in files:
            sr, signal = wavread(f)
            ceps, mspec, spec = mfcc(signal, nwin=2048, nfft=2048, fs=sr, nceps=coeffs)
            for i in range(ceps.shape[0]):
                dataset.appendLinked(ceps[i], [sex])

    tstdata, trndata = dataset.splitWithProportion(0.25)
    trndata._convertToOneOfMany()
    tstdata._convertToOneOfMany()

    print "Number of training patterns: ", len(trndata)
    print "Number of test patterns:     ", len(tstdata)
    print "Input and output dimensions: ", trndata.indim, trndata.outdim

    print "Train network..."
    fnn = buildNetwork(coeffs, int(coeffs*1.5), 2, outclass=SoftmaxLayer, fast=True)
    trainer = BackpropTrainer(fnn, dataset=trndata, learningrate=0.005)

    report_error(trainer, trndata, tstdata)
    for i in range(100):
        trainer.trainEpochs(1)
        report_error(trainer, trndata, tstdata)

if __name__ == '__main__':
    main("/path/to/hyke/audio_data")

1 Azarias Reda、Saurabh Panjwani 和 Edward Cutrell：Hyke：面向发展中地区的低成本远程考勤跟踪系统，第五届 ACM 发展中地区网络系统研讨会 (NSDR)。

于 2015-08-31T13:21:06.817 回答