matlab代码,一段含有噪声的语音信号,利用相关图法的三角窗和矩形窗进行噪声功率谱估计,并画出功率谱曲线
时间: 2024-02-25 20:59:43 浏览: 19
好的,以下是 Matlab 代码实现:
```matlab
% 读入语音信号
[y, Fs] = audioread('speech_signal.wav');
% 信号分帧
win_length = 256;
hop_length = 128;
frame_num = fix((length(y) - win_length) / hop_length) + 1;
frames = zeros(win_length, frame_num);
for i = 1:frame_num
frames(:,i) = y((i-1)*hop_length+1:(i-1)*hop_length+win_length);
end
% 计算每帧的功率谱
NFFT = 512;
power_spec_tri = zeros(NFFT/2+1, frame_num);
power_spec_rect = zeros(NFFT/2+1, frame_num);
for i = 1:frame_num
% 三角窗
tri_win = triang(win_length);
tri_win_frames = frames(:,i) .* tri_win;
tri_power_spec = abs(fft(tri_win_frames, NFFT)).^2 / (NFFT * sum(tri_win.^2));
power_spec_tri(:,i) = tri_power_spec(1:NFFT/2+1);
% 矩形窗
rect_win = rectwin(win_length);
rect_win_frames = frames(:,i) .* rect_win;
rect_power_spec = abs(fft(rect_win_frames, NFFT)).^2 / (NFFT * sum(rect_win.^2));
power_spec_rect(:,i) = rect_power_spec(1:NFFT/2+1);
end
% 计算平均功率谱
mean_power_spec_tri = mean(power_spec_tri, 2);
mean_power_spec_rect = mean(power_spec_rect, 2);
% 绘制功率谱曲线
f = (0:NFFT/2) / NFFT * Fs / 2;
plot(f, 10*log10(mean_power_spec_tri), 'r');
hold on;
plot(f, 10*log10(mean_power_spec_rect), 'b');
xlabel('Frequency (Hz)');
ylabel('Power (dB)');
legend('Triangle window', 'Rectangle window');
```
其中,`speech_signal.wav` 是一段含有噪声的语音信号,`win_length` 和 `hop_length` 分别是每帧的长度和帧移,`NFFT` 是 FFT 的点数。代码中首先将语音信号进行分帧,然后分别用三角窗和矩形窗加窗,再进行 FFT 得到每帧的功率谱,最后求平均功率谱并绘制功率谱曲线。