通过预加重，分帧，加窗，快速傅里叶变换，求谱线能量，通过梅尔滤波器组得到肺音lung.wav的梅尔谱图，横坐标为lung.wav的时间，纵坐标为频率，python代码

以下是基于Python的肺音梅尔谱图提取代码：

import numpy as np
import scipy.signal as signal
import scipy.io.wavfile as wavfile

# 定义预处理参数
pre_emphasis = 0.97
frame_size = 0.025
frame_stride = 0.01
NFFT = 512
nfilt = 40

# 定义梅尔滤波器组参数
low_freq_mel = 0
high_freq_mel = (2595 * np.log10(1 + (wavfile.read('lung.wav')[0] / 2) / 700))  # 根据Nyquist频率计算高频
mel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2)  # 均匀分布梅尔刻度
hz_points = (700 * (10 ** (mel_points / 2595) - 1))  # 将梅尔刻度转换为赫兹刻度
bin = np.floor((NFFT + 1) * hz_points / wavfile.read('lung.wav')[0])

# 定义加窗函数
hamming = np.hamming

# 加载音频文件
signal, sr = wavfile.read('lung.wav')

# 预加重
emphasized_signal = np.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])

# 分帧
frame_length, frame_step = frame_size * sr, frame_stride * sr
signal_length = len(emphasized_signal)
frame_length = int(round(frame_length))
frame_step = int(round(frame_step))
num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))

pad_signal_length = num_frames * frame_step + frame_length
z = np.zeros((pad_signal_length - signal_length))
pad_signal = np.append(emphasized_signal, z)

indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(
    np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
frames = pad_signal[indices.astype(np.int32, copy=False)]

# 加窗
frames *= hamming(frame_length)

# 快速傅里叶变换
mag_frames = np.absolute(np.fft.rfft(frames, NFFT))  # Magnitude of the FFT
pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2))  # Power Spectrum

# 建立梅尔滤波器组
f_bank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))
for m in range(1, nfilt + 1):
    f_m_minus = int(bin[m - 1])  # left
    f_m = int(bin[m])  # center
    f_m_plus = int(bin[m + 1])  # right

    for k in range(f_m_minus, f_m):
        f_bank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
    for k in range(f_m, f_m_plus):
        f_bank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])

# 应用梅尔滤波器组
filter_banks = np.dot(pow_frames, f_bank.T)

# 取对数
filter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks)  # 防止出现log(0)的情况
filter_banks = 20 * np.log10(filter_banks)  # dB

# 可视化梅尔谱图
import matplotlib.pyplot as plt

plt.imshow(filter_banks.T, cmap="hot", origin="lower")
plt.xlabel("Time")
plt.ylabel("Frequency")
plt.title("Mel Spectrogram")
plt.colorbar()
plt.show()

运行以上代码后将会生成肺音lung.wav的梅尔谱图。