1 模型
2 部分代码
function trimmed_X = my_vad(x)
%端点检测；输入为录入语音，输出为有用信号
Ini = 0.1; %初始静默时间
Ts = 0.01; %窗的时长
Tsh = 0.005; %帧移时长
Fs = 16000; %采样频率
counter1 = 0; %以下四个参数用来寻找起始点和结束点
counter2 = 0;
counter3 = 0;
counter4 = 0;
ZCRCountf = 0; %用于存储过零率检测结果
ZCRCountb = 0;
ZTh = 40; %过零阈值
w_sam = fix(Ts*Fs); %窗口长度
o_sam = fix(Tsh*Fs); %帧移长度
lengthX = length(x);
segs = fix((lengthX-w_sam)/o_sam)+1; %分帧数
sil = fix((Ini-Ts)/Tsh)+1; %静默时间帧数
win = hamming(w_sam);
Limit = o_sam*(segs-1)+1; %最后一帧的起始位置
FrmIndex = 1:o_sam:Limit; %每一帧的起始位置
ZCR_Vector = zeros(1,segs); %记录每一帧的过零点数
%短时过零点
for t = 1:segs
ZCRCounter = 0;
nextIndex = (t-1)*o_sam+1;
for r = nextIndex+1:(nextIndex+w_sam-1)
if (x(r) >= 0) && (x(r-1) >= 0)
elseif (x(r) > 0) && (x(r-1) < 0)
ZCRCounter = ZCRCounter + 1;
elseif (x(r) < 0) && (x(r-1) < 0)
elseif (x(r) < 0) && (x(r-1) > 0)
ZCRCounter = ZCRCounter + 1;
end
end
ZCR_Vector(t) = ZCRCounter;
end
%短时平均幅度
Erg_Vector = zeros(1,segs);
for u = 1:segs
nextIndex = (u-1)*o_sam+1;
Energy = x(nextIndex:nextIndex+w_sam-1).*win;
Erg_Vector(u) = sum(abs(Energy));
end
IMN = mean(Erg_Vector(1:sil)); %静默能量均值（噪声均值）
IMX = max(Erg_Vector); %短时平均幅度的最大值
I1 = 0.03 * (IMX-IMN) + IMN; %I1，I2为初始能量阈值
I2 = 4 * IMN;
ITL = 100*min(I1,I2); %能量阈值下限,前面系数根据实际情况更改得到合适结果
ITU = 10* ITL; %能量阈值上限
IZC = mean(ZCR_Vector(1:sil));
stdev = std(ZCR_Vector(1:sil)); %静默阶段过零率标准差
IZCT = min(ZTh,IZC+2*stdev); %过零率阈值
indexi = zeros(1,lengthX);
indexj = indexi;
indexk = indexi;
indexl = indexi;
%搜寻超过能量阈值上限的部分
for i = 1:length(Erg_Vector)
if (Erg_Vector(i) > ITU)
counter1 = counter1 + 1;
indexi(counter1) = i;
end
end
ITUs = indexi(1); %第一个能量超过阈值上限的帧
%搜寻能量超过能量下限的部分
for j = ITUs:-1:1
if (Erg_Vector(j) < ITL)
counter2 = counter2 + 1;
indexj(counter2) = j;
end
end
start = indexj(1)+1; %第一级判决起始帧
Erg_Vectorf = fliplr(Erg_Vector);%将能量矩阵关于中心左右对称，如果是一行向量相当于逆序
%重复上面过程相当于找结束帧
for k = 1:length(Erg_Vectorf)
if (Erg_Vectorf(k) > ITU)
counter3 = counter3 + 1;
indexk(counter3) = k;
end
end
ITUf = indexk(1);
for l = ITUf:-1:1
if (Erg_Vectorf(l) < ITL)
counter4 = counter4 + 1;
indexl(counter4) = l;
end
end
finish = length(Erg_Vector)-indexl(1)+1;%第一级判决结束帧
%从第一级判决起始帧开始进行第二判决（过零率）端点检测
BackSearch = min(start,25);
for m = start:-1:start-BackSearch+1
rate = ZCR_Vector(m);
if rate > IZCT
ZCRCountb = ZCRCountb + 1;
realstart = m;
end
end
if ZCRCountb > 3
start = realstart;
end
FwdSearch = min(length(Erg_Vector)-finish,25);
for n = finish+1:finish+FwdSearch
rate = ZCR_Vector(n);
if rate > IZCT
ZCRCountf = ZCRCountf + 1;
realfinish = n;
end
end
if ZCRCountf > 3
finish = realfinish;
end
x_start = FrmIndex(start); %最终的起始位置
x_finish = FrmIndex(finish-1); %最终的结束位置
trimmed_X = x(x_start:x_finish);
3 仿真结果
4 参考文献
[1]张军, & 李学斌. (2009). 一种基于dtw的孤立词语音识别算法. 计算机仿真(10), 358-361+374.
