【sklearn】GMM識別柏林情感語音庫

參考

• Emotion-Recognition-from-Speech
• sklearn官方GMM在鳶尾花上應用實例
• 特徵及標籤均根據emotion-recognition-from-speech生產的cPickle文件獲得
• 分類器的使用參考sklearn

出現問題

在使用estimator.fit()的過程中，出現float() argument must be a string or a number 錯誤。經檢查，是mean_init邏輯錯誤，導致模型無初始化平均值。

代碼v1.0

# -*- coding:utf-8 -*-
import cPickle
import matplotlib as mpl
import matplotlib.pyplot as plt
from dataset import *

import numpy as np

from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold

colors = ['aliceblue', 'aquamarine', 'brown', 'coral',
    'cyan', 'dimgray', 'fuchsia']

def make_ellipses(gmm, ax):
    for n, color in enumerate(colors):
        if gmm.covariance_type == 'full':
            covariances = gmm.covariances_[n][:2, :2]
        elif gmm.covariance_type == 'tied':
            covariances = gmm.covariances_[:2, :2]
        elif gmm.covariance_type == 'diag':
            covariances = np.diag(gmm.covariances_[n][:2])
        elif gmm.covariance_type == 'spherical':
            covariances = np.eye(gmm.means_.shape[1])*gmm.covariances_[n]
        v,w = np.linalg.eigh(covariances)
        u = w[0] / np.linalg.norm(w[0])
        angle = np.arctan2(u[1], u[0])
        angle = 180 * angle / np.pi
        v = 2. * np.sqrt(2.) * np.sqrt(v)
        ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],
            180 + angle, color=color)
        ell.set_clip_box(ax.bbox)
        ell.set_alpha(0.5)
        ax.add_artist(ell)

def main():
    with open('berlin_features.p', 'rb') as f_Fglobal:
        Fglobal = cPickle.load(f_Fglobal)
    f_Fglobal.close()
    #Fglobal = cPickle.load(open('berlin_features.p', 'rb'))
    db = cPickle.load(open('berlin_db.p','rb'))
    y = np.array(db.targets)
    #y = np.array([0,1,2,3,4,5,6])
    classes = {0:'W', 1:'L', 2:'E', 3:'A', 4:'F', 5:'T', 6:'N'}
    skf = StratifiedKFold(n_splits=4)
    train_index, test_index = next(iter(skf.split(Fglobal,y)))

    X_train = np.array(Fglobal)[train_index]
    print X_train
    print type(X_train)
    Y_train = np.array(y)[train_index]

    X_test = np.array(Fglobal)[test_index]
    Y_test = np.array(y)[test_index]

    estimators = dict((cov_type,GaussianMixture(n_components=7,
        covariance_type=cov_type, max_iter=100, random_state=0))
        for cov_type in ['spherical', 'diag', 'tied', 'full'])

    n_estimators = len(estimators)

    plt.figure(figsize=( 3 * n_estimators //2, 6))
    plt.subplots_adjust(bottom=0.01, top=0.95, hspace=.15, wspace=.05,
        left=.01, right=.99)
    for index, (name, estimator) in enumerate(estimators.items()):
        estimator.means_init = np.array([X_train[Y_train==i].mean(axis=0)
            for i in range(7)])

        # estimator.fit(x)
        estimator.fit(X_train)

        h = plt.subplot(2, n_estimators //2, index + 1)
        make_ellipses(estimator, h)

        for n,color in enumerate(colors):
            data = np.array(Fglobal)[y == n]
            plt.scatter(data[:, 0], data[:, 1], s=0.8, color=color,
                label=classes[n])

        for n,color in enumerate(colors):
            data = X_test[Y_test==n]
            plt.scatter(data[:, 0], data[:, 1], marker='x', color=color)

        y_train_pred = estimator.predict(X_train)
        train_accuracy = np.mean(y_train_pred.ravel() == Y_train.ravel()) *100
        plt.text(0.05, 0.9, 'train_accuracy: %.1f' %train_accuracy,
            transform=h.transAxes)

        y_test_pred = estimator.predict(X_test)
        test_accuracy = np.mean(y_test_pred.ravel() == Y_test.ravel()) * 100
        plt.text(0.05, 0.8, 'test_accuracy: %.1f' %test_accuracy,
            transform=h.transAxes)

        plt.xticks(())
        plt.yticks(())
        plt.title(name)
    plt.legend(scatterpoints=1, loc='lower right', prop=dict(size=12))
    plt.show()


main()

其中dataset.py出自emotion項目

結果

識別率50%左右