写一段MFCC算法，要求将其输出的特征值转换为整数

以下是一个简单的MFCC算法示例，它使用Python实现，可以将输出的特征值转换为整数：

import numpy as np
import scipy.fftpack as fft
from scipy.signal import hamming
from scipy.fftpack.realtransforms import dct

# 常数定义
num_mel_filters = 26
num_cepstral_coeffs = 13
frame_length = 0.025 # 25ms
frame_step = 0.010 # 10ms
sample_rate = 16000
lower_edge_frequency = 0
upper_edge_frequency = sample_rate / 2

# 预处理Hamming窗函数
hamming_window = hamming(int(frame_length * sample_rate))
def pre_emphasis(signal, alpha=0.97):
    # 预加重滤波器
    return np.append(signal[0], signal[1:] - alpha * signal[:-1])

def get_mel_filterbank(num_filters, fft_size, sample_rate, lower_freq, upper_freq):
    # 计算梅尔滤波器组
    mel_filterbank = np.zeros((num_filters, int(fft_size / 2 + 1)))
    mel_low_freq = 1125 * np.log(1 + lower_freq / 700)
    mel_high_freq = 1125 * np.log(1 + upper_freq / 700)
    mel_freq_points = np.linspace(mel_low_freq, mel_high_freq, num_filters + 2)
    hz_freq_points = 700 * (np.exp(mel_freq_points / 1125) - 1)
    bin_freq_points = np.floor((fft_size + 1) * hz_freq_points / sample_rate)

    for i in range(0, num_filters):
        left = int(bin_freq_points[i])
        center = int(bin_freq_points[i+1])
        right = int(bin_freq_points[i+2])
        for j in range(left, center):
            mel_filterbank[i, j] = (j - bin_freq_points[i]) / (bin_freq_points[i+1] - bin_freq_points[i])
        for j in range(center, right):
            mel_filterbank[i, j] = (bin_freq_points[i+2] - j) / (bin_freq_points[i+2] - bin_freq_points[i+1])

    return mel_filterbank

def mfcc(signal):
    # 获取预加重信号
    pre_emphasis_signal = pre_emphasis(signal)

    # 将信号分帧
    frame_length_sample = int(frame_length * sample_rate)
    frame_step_sample = int(frame_step * sample_rate)
    signal_length = len(pre_emphasis_signal)
    num_frames = int(np.ceil(float(np.abs(signal_length - frame_length_sample)) / frame_step_sample))

    padsignal_length = num_frames * frame_step_sample + frame_length_sample
    padsignal = np.zeros((padsignal_length,))
    padsignal[:signal_length] = pre_emphasis_signal
    indices = np.tile(np.arange(0, frame_length_sample), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step_sample, frame_step_sample), (frame_length_sample, 1)).T
    frames = padsignal[indices.astype(np.int32, copy=False)]

    # 加窗
    windowed_frames = frames * hamming_window

    # 傅里叶变换
    NFFT = 512
    mag_frames = np.absolute(np.fft.rfft(windowed_frames, NFFT))  # Magnitude of the FFT
    pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2))  # Power Spectrum

    # 计算Mel滤波器组
    mel_filterbank = get_mel_filterbank(num_mel_filters, NFFT, sample_rate, lower_edge_frequency, upper_edge_frequency)

    # 应用Mel滤波器组
    mel_filtered_frames = np.dot(pow_frames, mel_filterbank.T)

    # 取对数
    log_mel_filtered_frames = 20 * np.log10(mel_filtered_frames)

    # 应用离散余弦变换
    dct_frames = dct(log_mel_filtered_frames, type=2, axis=1, norm='ortho')[:, 1:(num_cepstral_coeffs + 1)]

    # 将特征值转换为整数
    dct_frames = dct_frames * 10000
    dct_frames = dct_frames.astype(np.int32)

    return dct_frames

这个算法首先对信号进行预加重，然后将其分帧，并应用Hamming窗来减少频谱泄露。接下来，对每一帧进行傅里叶变换，然后计算Mel滤波器组并应用它们。将结果取对数，然后应用离散余弦变换以获得MFCC系数。最后，将得到的特征值乘以一个大的数（例如10000），并将其转换为整数。