写一段matlab代码,提取语音信号的mfcc特征(语音信号存储在audio_data中
时间: 2024-05-15 12:16:33 浏览: 11
):
```
% 设置参数
fs = 8000; % 采样率
win_size = 256; % 窗口大小
hop_size = 128; % 帧移大小
num_mfcc = 12; % MFCC系数个数
% 提取MFCC特征
mfccs = mfcc(audio_data, fs, 'WindowLength', win_size, 'OverlapLength', win_size-hop_size, 'NumCoeffs', num_mfcc);
% 显示MFCC特征
imagesc(mfccs);
xlabel('帧数');
ylabel('MFCC系数');
```
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写一段matlab代码,提取语音信号的mfcc特征(语音信号存储在audio_data中)
以下是一个matlab函数,可以提取语音信号的mfcc特征:
function mfcc_feature = extract_mfcc(audio_data, fs)
% 输入参数:
% audio_data: 语音信号
% fs: 采样率
% 输出参数:
% mfcc_feature: MFCC特征
% 预处理:去除静音段、标准化音量等
audio_data = remove_silence(audio_data);
audio_data = normalize_volume(audio_data);
% 分帧
frame_length = 0.025; % 帧长(秒)
frame_shift = 0.01; % 帧移(秒)
frame_size = round(frame_length * fs); % 帧长(采样点)
frame_step = round(frame_shift * fs); % 帧移(采样点)
frames = enframe(audio_data, frame_size, frame_step);
% 加窗
window = hamming(frame_size);
frames = bsxfun(@times, frames, window');
% 傅里叶变换
nfft = 2^nextpow2(frame_size);
spectra = abs(fft(frames, nfft, 2));
% 梅尔滤波器组
num_filters = 20; % 滤波器个数
low_freq = 0; % 最低频率(赫兹)
high_freq = fs / 2; % 最高频率(赫兹)
mel_filter_bank = mel_filter(num_filters, nfft, fs, low_freq, high_freq);
% 滤波
filtered_spectra = mel_filter_bank * spectra';
% 去除直流分量
filtered_spectra = filtered_spectra(2:end, :);
% 取对数
log_filtered_spectra = log(filtered_spectra);
% 进行离散余弦变换(DCT)
num_ceps = 12; % MFCC系数个数
dct_matrix = dctmtx(num_filters);
mfcc_feature = dct_matrix(1:num_ceps, :) * log_filtered_spectra;
end
其中,remove_silence和normalize_volume是预处理函数,enframe是分帧函数,mel_filter是梅尔滤波器函数。可以在网上找到这些函数的实现代码。
写一段完整的matlab代码,提取语音信号的mfcc特征(语音信号存储在audio_data中)
% 导入语音信号数据
load('audio_data.mat');
% 设定参数
fs = 8000; % 采样率
winlen = 0.025; % 窗长
hoplen = 0.01; % 帧移
nfft = 256; % FFT点数
numcep = 13; % MFCC系数数目
lifter = 22; % 倒谱提升系数
% 对语音信号进行预处理
audio_data = audio_data / max(abs(audio_data)); % 归一化
audio_data = filter([1 -0.97], 1, audio_data); % 预加重
% 分帧
frame_len = round(winlen*fs);
frame_hop = round(hoplen*fs);
frames_num = floor((length(audio_data)-frame_len)/frame_hop) + 1;
frames = zeros(frame_len,frames_num);
for i = 1:frames_num
start_index = (i-1)*frame_hop + 1;
end_index = start_index + frame_len - 1;
frames(:,i) = audio_data(start_index:end_index);
end
% 加窗
window = hamming(frame_len);
frames = frames .* repmat(window,1,frames_num);
% 计算功率谱
mag_frames = abs(fft(frames,nfft));
pow_frames = (1/nfft) * (mag_frames.^2);
% 建立Mel滤波器组
low_freq_mel = 0;
high_freq_mel = 2595*log10(1+(fs/2)/700);
mel_points = linspace(low_freq_mel,high_freq_mel,23);
hz_points = 700*(10.^(mel_points/2595)-1);
bin = floor((nfft+1)*hz_points/fs);
filter_bank = zeros(numcep,nfft/2+1);
for j = 1:numcep
for i = 1:(bin(j+1)-bin(j))
filter_bank(j,bin(j)+i) = (i-1)/(bin(j+1)-bin(j));
end
for i = (bin(j+1)-bin(j)+1):(bin(j+2)-bin(j+1))
filter_bank(j,bin(j)+i) = (bin(j+2)-i)/(bin(j+2)-bin(j+1));
end
end
% 计算MFCC系数
mel_energies = filter_bank * pow_frames(1:nfft/2+1,:);
log_mel_energies = log10(mel_energies + eps);
mfcc_coeffs = dct(log_mel_energies);
% 提升MFCC系数
cep_lifter = (1:numcep)';
cep_lifter = 1 + (lifter/2)*sin(pi*cep_lifter/lifter);
mfcc_coeffs = mfcc_coeffs .* repmat(cep_lifter,1,frames_num);
% 取前13个MFCC系数
mfcc_coeffs = mfcc_coeffs(1:numcep,:);
% 显示MFCC系数
disp(mfcc_coeffs);