用python代码将提取的MFCC特征生成可视化png图像

您可以使用Python中的matplotlib库来生成可视化的png图像。以下是一个示例代码，它从提取的MFCC特征中生成一张png图像：

import matplotlib.pyplot as plt
import numpy as np

# 从文件中读取MFCC特征
mfcc_features = np.load('mfcc_features.npy')

# 生成可视化图像
plt.imshow(mfcc_features, cmap='hot', interpolation='nearest')
plt.xlabel('MFCC Coefficients')
plt.ylabel('Frames')
plt.title('MFCC Feature Visualization')
plt.savefig('mfcc_visualization.png')

在这个示例代码中，我们首先使用numpy库从文件中读取MFCC特征数据，然后使用matplotlib库中的imshow函数将其可视化为一张热力图。接下来，我们添加了一些标签和标题，最后使用savefig函数将图像保存为png文件。

请注意，这只是一个简单的示例代码，您需要根据自己的数据格式和需求进行修改。另外，生成的图像可能需要进行进一步的调整和美化，以更好地呈现MFCC特征。

相关问题

在下面代码中修改添加一个可视化图，用来画出r经过t_sne之后前15行和15到30行数据的可视化图。import pandas as pd from sklearn import cluster from sklearn import metrics import matplotlib.pyplot as plt from sklearn.manifold import TSNE from sklearn.decomposition import PCA def k_means(data_set, output_file, png_file, png_file1, t_labels, score_file, set_name): model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++") model.fit(data_set) # print(list(model.labels_)) p_labels = list(model.labels_) r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1) r.columns = list(data_set.columns) + [u'聚类类别'] print(r) # r.to_excel(output_file) with open(score_file, "a") as sf: sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n") sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n") '''pca = PCA(n_components=2) pca.fit(data_set) pca_result = pca.transform(data_set) t_sne = pd.DataFrame(pca_result, index=data_set.index)''' t_sne = TSNE() t_sne.fit(data_set) t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False dd = t_sne[r[u'聚类类别'] == 0] plt.plot(dd[0], dd[1], 'r.') dd = t_sne[r[u'聚类类别'] == 1] plt.plot(dd[0], dd[1], 'go') dd = t_sne[r[u'聚类类别'] == 2] plt.plot(dd[0], dd[1], 'b*') dd = t_sne[r[u'聚类类别'] == 3] plt.plot(dd[0], dd[1], 'o') dd = t_sne[r[u'聚类类别'] == 4] plt.plot(dd[0], dd[1], 'm.') dd = t_sne[r[u'聚类类别'] == 5] plt.plot(dd[0], dd[1], 'co') dd = t_sne[r[u'聚类类别'] == 6] plt.plot(dd[0], dd[1], 'y*') plt.savefig(png_file) plt.clf() '''plt.scatter(data_set.iloc[:, 0], data_set.iloc[:, 1], c=model.labels_) plt.savefig(png_file) plt.clf()''' frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv") tLabel = [] for family in frog_data['name']: if family == "A": tLabel.append(0) elif family == "B": tLabel.append(1) elif family == "C": tLabel.append(2) elif family == "D": tLabel.append(3) elif family == "E": tLabel.append(4) elif family == "F": tLabel.append(5) elif family == "G": tLabel.append(6) scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt" first_set = frog_data.iloc[:, 1:1327] k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", "D:/PyCharmPython/pythonProject/kMeansSet_2_1.png", tLabel, scoreFile, "Set_1")

您可以使用以下代码来添加可视化图来画出经过t-sne处理后的前15行和15到30行数据的可视化图：

import pandas as pd
from sklearn import cluster
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE

from sklearn.decomposition import PCA

def k_means(data_set, output_file, png_file, png_file1, t_labels, score_file, set_name):
    model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++")
    model.fit(data_set)
    p_labels = list(model.labels_)
    r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1)
    r.columns = list(data_set.columns) + [u'聚类类别']
    print(r)
    with open(score_file, "a") as sf:
        sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n")
        sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n")
    
    t_sne = TSNE()
    t_sne.fit(data_set)
    t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index)
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    dd = t_sne[r[u'聚类类别'] == 0]
    plt.plot(dd[0], dd[1], 'r.')
    dd = t_sne[r[u'聚类类别'] == 1]
    plt.plot(dd[0], dd[1], 'go')
    dd = t_sne[r[u'聚类类别'] == 2]
    plt.plot(dd[0], dd[1], 'b*')
    dd = t_sne[r[u'聚类类别'] == 3]
    plt.plot(dd[0], dd[1], 'o')
    dd = t_sne[r[u'聚类类别'] == 4]
    plt.plot(dd[0], dd[1], 'm.')
    dd = t_sne[r[u'聚类类别'] == 5]
    plt.plot(dd[0], dd[1], 'co')
    dd = t_sne[r[u'聚类类别'] == 6]
    plt.plot(dd[0], dd[1], 'y*')
    
    # 画出前15行数据的可视化图
    plt.savefig(png_file)
    plt.clf()
    
    # 画出15到30行数据的可视化图
    dd = t_sne.iloc[15:30, :]
    plt.plot(dd[0], dd[1], 'r.')
    plt.savefig(png_file1)
    plt.clf()

frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv")
tLabel = []
for family in frog_data['name']:
    if family == "A":
        tLabel.append(0)
    elif family == "B":
        tLabel.append(1)
    elif family == "C":
        tLabel.append(2)
    elif family == "D":
        tLabel.append(3)
    elif family == "E":
        tLabel.append(4)
    elif family == "F":
        tLabel.append(5)
    elif family == "G":
        tLabel.append(6)

scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt"
first_set = frog_data.iloc[:, 1:1327]
k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", "D:/PyCharmPython/pythonProject/kMeansSet_2_1.png", tLabel, scoreFile, "Set_1")

请将代码中的路径和文件名更改为您实际的路径和文件名。这段代码会生成两个可视化图，一个是前15行数据的图（保存为`png_file`），另一个是15到30行数据的图（保存为`png_file1`）。

在下面代码中添加一个可视化图，用来画出r经过t_sne之后前15行数据的图 import pandas as pd from sklearn import cluster from sklearn import metrics import matplotlib.pyplot as plt from sklearn.manifold import TSNE from sklearn.decomposition import PCA def k_means(data_set, output_file, png_file, png_file1, t_labels, score_file, set_name): model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++") model.fit(data_set) # print(list(model.labels_)) p_labels = list(model.labels_) r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1) r.columns = list(data_set.columns) + [u'聚类类别'] print(r) # r.to_excel(output_file) with open(score_file, "a") as sf: sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n") sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n") '''pca = PCA(n_components=2) pca.fit(data_set) pca_result = pca.transform(data_set) t_sne = pd.DataFrame(pca_result, index=data_set.index)''' t_sne = TSNE() t_sne.fit(data_set) t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False dd = t_sne[r[u'聚类类别'] == 0] plt.plot(dd[0], dd[1], 'r.') dd = t_sne[r[u'聚类类别'] == 1] plt.plot(dd[0], dd[1], 'go') dd = t_sne[r[u'聚类类别'] == 2] plt.plot(dd[0], dd[1], 'b*') dd = t_sne[r[u'聚类类别'] == 3] plt.plot(dd[0], dd[1], 'o') dd = t_sne[r[u'聚类类别'] == 4] plt.plot(dd[0], dd[1], 'm.') dd = t_sne[r[u'聚类类别'] == 5] plt.plot(dd[0], dd[1], 'co') dd = t_sne[r[u'聚类类别'] == 6] plt.plot(dd[0], dd[1], 'y*') plt.savefig(png_file) '''plt.scatter(data_set.iloc[:, 0], data_set.iloc[:, 1], c=model.labels_) plt.savefig(png_file) plt.clf()''' frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv") tLabel = [] for family in frog_data['name']: if family == "A": tLabel.append(0) elif family == "B": tLabel.append(1) elif family == "C": tLabel.append(2) elif family == "D": tLabel.append(3) elif family == "E": tLabel.append(4) elif family == "F": tLabel.append(5) elif family == "G": tLabel.append(6) scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt" first_set = frog_data.iloc[:, 1:1327] k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", "D:/PyCharmPython/pythonProject/kMeansSet_2_1.png", tLabel, scoreFile, "Set_1")

你可以在以下代码中添加一个可视化图来画出r经过t_sne之后前15行数据的图：

import pandas as pd
from sklearn import cluster
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA

def k_means(data_set, output_file, png_file, png_file1, t_labels, score_file, set_name):
    model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++")
    model.fit(data_set)
    p_labels = list(model.labels_)
    r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1)
    r.columns = list(data_set.columns) + [u'聚类类别']
    print(r)
    with open(score_file, "a") as sf:
        sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n")
        sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n")

    t_sne = TSNE()
    t_sne.fit(data_set)
    t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index)
    
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    dd = t_sne[r[u'聚类类别'] == 0]
    plt.plot(dd[0], dd[1], 'r.')
    dd = t_sne[r[u'聚类类别'] == 1]
    plt.plot(dd[0], dd[1], 'go')
    dd = t_sne[r[u'聚类类别'] == 2]
    plt.plot(dd[0], dd[1], 'b*')
    dd = t_sne[r[u'聚类类别'] == 3]
    plt.plot(dd[0], dd[1], 'o')
    dd = t_sne[r[u'聚类类别'] == 4]
    plt.plot(dd[0], dd[1], 'm.')
    dd = t_sne[r[u'聚类类别'] == 5]
    plt.plot(dd[0], dd[1], 'co')
    dd = t_sne[r[u'聚类类别'] == 6]
    plt.plot(dd[0], dd[1], 'y*')
    plt.savefig(png_file)

frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv")
tLabel = []
for family in frog_data['name']:
    if family == "A":
        tLabel.append(0)
    elif family == "B":
        tLabel.append(1)
    elif family == "C":
        tLabel.append(2)
    elif family == "D":
        tLabel.append(3)
    elif family == "E":
        tLabel.append(4)
    elif family == "F":
        tLabel.append(5)
    elif family == "G":
        tLabel.append(6)

scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt"
first_set = frog_data.iloc[:, 1:1327]
k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", "D:/PyCharmPython/pythonProject/kMeansSet_2_1.png", tLabel, scoreFile, "Set_1")

请确保你已经安装了所需的库，并且将文件路径修改为你实际的文件路径。这段代码会将数据集进行聚类，并使用t-SNE进行降维，然后绘制出前15行数据的聚类可视化图。保存的图像将会保存在`png_file`路径下。