- 我自己仿照capi_v2_gain，写了一个低通滤波的框架，基本只改变了process里面的核心算法函数，输入信号为包含500、1000和2000频率成分的正弦信号，但是在sdk中仿真处理完之后的信号没有实现滤波，并且包含很多高频成分。将代码集成到adsp中，手机的录音就出现很多杂音。
- 另外：这个定点计算的低通滤波我在matlab中仿真过，可以得到正确的结果，但放到sdk里就不对了，有可能是需要内存分配啥的，我是个c/c++和dsp小白，一直找不到问题所在，求大佬拯救一下！！！ 代码如下：
/*
in_buf[]是指向每个通道的数据缓冲区地址的指针数组，我设置的通道数（num_channels）为2，in_buf的数组元素数量为2；
N是滤波器阶数
*/
void lowpass_filter(int32_t* in_buf[],
int32_t* out_buf[],
uint16_t N,
uint32_t num_samples)
{
uint32_t h,i,j,k;
int64_t out;
int32_t coef;//定点化后的系数
uint16_t Q = 13; //将滤波系数定点化的定标位
//matlab中得到的1khz的低通滤波系数
const float filter_coefficients[101] = {0.0001313106797282,6.834005229901e-05,-1.353104057844e-19,-7.762389720212e-05,
-0.0001685125401241,-0.0002764357282808,-0.0004046614304012,-0.0005556629350297,
-0.0007308354291817,-0.000930233679387,-0.001152342207602,-0.001393888663946,
-0.001649710012508,-0.001912679692733,-0.002173702137323,-0.002421778970265,
-0.00264414893783,-0.002826501212082,-0.002953259227455,-0.003007929747013,
-0.002973509487934,-0.002832939445581,-0.002569595118286, -0.00216779921951,
-0.001613342230416,-0.0008939953422497,9.204641745017e-19, 0.001075481592696,
0.002335970006679, 0.003781340034815, 0.005407572350892, 0.007206595681381,
0.00916622743461, 0.01127021808002, 0.01349840170039, 0.01582695213701,
0.01822874110185, 0.02067379163472, 0.02312981742882, 0.02556283592438,
0.02793784075965, 0.03021951724431, 0.03237298304651, 0.03436453530788,
0.03616238495784, 0.03773735910487, 0.0390635530409, 0.0401189145893,
0.0408857452251, 0.04135110454983, 0.04150710725172, 0.04135110454983,
0.0408857452251, 0.0401189145893, 0.0390635530409, 0.03773735910487,
0.03616238495784, 0.03436453530788, 0.03237298304651, 0.03021951724431,
0.02793784075965, 0.02556283592438, 0.02312981742882, 0.02067379163472,
0.01822874110185, 0.01582695213701, 0.01349840170039, 0.01127021808002,
0.00916622743461, 0.007206595681381, 0.005407572350892, 0.003781340034815,
0.002335970006679, 0.001075481592696,9.204641745017e-19,-0.0008939953422497,
-0.001613342230416, -0.00216779921951,-0.002569595118286,-0.002832939445581,
-0.002973509487934,-0.003007929747013,-0.002953259227455,-0.002826501212082,
-0.00264414893783,-0.002421778970265,-0.002173702137323,-0.001912679692733,
-0.001649710012508,-0.001393888663946,-0.001152342207602,-0.000930233679387,
-0.0007308354291817,-0.0005556629350297,-0.0004046614304012,-0.0002764357282808,
-0.0001685125401241,-7.762389720212e-05,-1.353104057844e-19,6.834005229901e-05,
0.0001313106797282};
//h是num_channels,即通道数，num_samples是每帧采样点
for (h = 0; h < 2; h++)
{
for (i = 0; i < num_samples; i++)
{
//TODO:
k = (i<N)?i:N;
out = 0;
for (j = 0; j <= k; j++)
{
coef = int16_t((filter_coefficients[j])* (float)((int32_t)(1)<<Q));
out += int32_t(coef) * int32_t((in_buf[h][i-j]));
}
out = ((out >> Q));
out_buf[h][i] = int32_t(out) ;
}
}
}
https://blog.csdn.net/u010182064/article/details/41754357