C++ DSP library for MATLAB similar programming.
This is a library for those who hate this kind of code:
std::transform(x.begin(), x.end(), x.begin(), [](auto& v){
return v * 0.3;
});
auto power = std::accumulate(x.begin() + lb, x.begin() + rb, 0.0, [](double accum, const std::complex<double>& v) {
return accum + (v.real() * v.real() + v.imag() * v.imag());
});
auto r = std::vector<double>(x1.size());
for (int i=0; i < r.size(); ++i) {
r[i] = x1[i] * x2[i];
}
auto p = fftw_plan_dft_1d(N, x, spec, FFTW_FORWARD, FFTW_ESTIMATE);
fftw_execute(p);
fftw_destroy_plan(p);and who likes this:
using namespace dsplib;
x *= 0.3;
auto power = sum(abs2(*x.slice(lb, rb)));
auto r = x1 * x2;
auto spec = fft(x);| dsplib | matlab | numpy |
|---|---|---|
| x * x | x .* x | x * x |
| zeros(20) | zeros(20, 1) | zeros(20) |
| x.slice(0,10) = 1 | x(1:10) = 1 | x[0:10] = 1 |
| x.slice(2,end) = 1 | x(3:end) = 1 | x[2:] = 1 |
| x.slice(2, 4) = {1, 2} | x(3:4) = [1, 2] | x[2:4] = [1, 2] |
| x.slice(0, -1) | x(1:end-1) | x[0:-1] |
Only 1-D arrays with element-wise operations are currently supported.
dsplib::real_t v1;
dsplib::cmplx_t v2;
v1 = 10;
v2 = v1;
v2 = 10-10i;
v2 = {10, -10};
v2.re = 10;
v2.im = -10;
v2 = std::complex<double>(10, -10);using namespace dsplib;
arr_real x1 = {0, 1, 2, 3, 4};
arr_cmplx x2 = {1i, 1+2i, 4, -5-5i};
x2.slice(2, 4) = {1i, 2i};
arr_real y1 = x1 * x1;
arr_real y2 = x1 * 1000;
arr_cmplx y3 = x1 * x2;
arr_cmplx y4 = x2 * 1000;
arr_cmplx y5 = x2.slice(0, 2);
arr_cmplx y6 = x1 * 2i;The behavior of slices is as close as possible to numpy. Except for cases with invalid indexes, in which case numpy does not throw an exception.
arr_real x = {0, 1, 2, 3, 4, 5, 6};
x.slice(0, 2) ///{0, 1}
x.slice(2, -1) ///{2, 3, 4, 5}
x.slice(-1, 0, -1) ///{6, 5, 4, 3, 2, 1}
x.slice(-1, 0) ///OUT_OF_RANGE, but numpy returns []
x.slice(0, -1, -1) ///OUT_OF_RANGE, but numpy returns []
x.slice(-8, 7) ///OUT_OF_RANGE, but numpy returns [0 1 2 3 4 5 6]The FFT/IFFT calculation table is cached on first run. To eliminate this behavior, you can use the FftPlan object.
arr_real x = randn(500);
arr_cmplx y1 = fft(x); //500
arr_cmplx y2 = fft(x, 1024); //1024arr_cmplx x = 1i * zeros(512);
x[10] = 1;
arr_cmplx y = ifft(x);const auto h = fir1(100, 0.1, FilterType::Low);
auto flt = FftFilter(h);
arr_real x = randn(10000);
arr_real y = flt(x);auto flt = HilbertFilter();
arr_real x = randn(10000);
arr_cmplx y1 = flt(x);
//or
arr_cmplx y2 = hilbert(x);arr_real x = randn(10000);
arr_real y = awgn(x, 10); //snr=10dBarr_real x1 = randn(500);
arr_real x2 = awgn(x1, 10);
arr_real y = xcorr(x1, x2);arr_real x1 = randn(500);
arr_real x2 = delayseq(x1, 100);
auto d1 = finddelay(x1, x2); ///d1=100
auto [d2, _] = gccphat(x2, x1); ///d2=100.0const int n = 1024;
arr_real x = randn(n);
x *= window::hann(n);
arr_cmplx y = fft(x) / (n/2);
y = y.slice(0, n/2+1);
auto z = pow2db(abs2(y)); //20*log10(..)auto x = randn(10000);
auto h = fir1(99, 0.1, 0.2, FilterType::Bandstop);
auto flt = FirFilter(h);
auto y = flt(x);//simulate room impulse response
int M = 50;
auto rir = fir1(M-1, 0.1);
auto flt = FirFilter(rir);
int L = 100000;
auto x = randn(L); //ref signal
auto n = 0.01 * randn(L);
auto d = flt(x) + n; //mic signal
auto rls = RlsFilterR(M, 0.98, 1000);
auto [y, e] = rls(x, d);
ASSERT_LE(nmse(flt.coeffs(), rir), 0.01);To process signal in batches (for example, in real time), use modules FIRDecimator, FIRInterpolator, FIRRateConverter or FIRResampler. The output signal will be delayed, but there will be no gap between frames.
To process the entire signal (for example, from a file), use function resample(x, p, q). The processing result will be aligned.
//44100 -> 16000
auto rsmp = dsplib::FIRResampler(16000, 44100);
auto out = rsmp.process(in);
//or
auto rsmp = dsplib::FIRRateConverter(160, 441);
auto out = rsmp.process(in);
//or
auto out = dsplib::resample(in, 16000, 44100);
//or
auto out = dsplib::resample(in, 160, 441);//32000 -> 16000
auto decim = dsplib::FIRDecimator(2);
auto out = decim.process(in);
//or
auto out = dsplib::resample(in, 1, 2);
//or
auto out = dsplib::resample(in, 16000, 32000);//16000 -> 32000
auto interp = dsplib::FIRInterpolator(2);
auto out = interp.process(in);
//or
auto out = dsplib::resample(in, 2, 1);
//or
auto out = dsplib::resample(in, 32000, 16000);- CMake (>=3.10)
- C++ compiler for C++17 standard (gcc, clang, msvc, mingw)
# set DSPLIB_USE_FLOAT32=ON to enable float base type (double by default)
# set DSPLIB_NO_EXCEPTIONS=ON to disable exceptions
# set BUILD_SHARED_LIBS=ON to build shared lib
cmake . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --target=installCPMAddPackage(NAME dsplib
GIT_REPOSITORY
"https://github.com/vitalsong/dsplib.git"
VERSION
0.45.0
OPTIONS
"DSPLIB_USE_FLOAT32 OFF"
"DSPLIB_NO_EXCEPTIONS OFF"
EXCLUDE_FROM_ALL ON
)
target_link_libraries(${PROJECT_NAME} dsplib)To build and run benchmarks:
cmake . -B build -DCMAKE_BUILD_TYPE=Release -DDSPLIB_BUILD_BENCHS=ON
cmake --build build
./build/benchs/dsplib-benchsThe implementation of non-power-of-two FFT is based on the general factorization algorithm. It is usually slower, but not critical.
For prime and semi-prime numbers, the czt algorithm is used, which can be significantly slower (but not as slow as regular DFT).
Use
FFT(N!=2^K)only if you know what you are doing.
Run on (12 X 2600 MHz CPU s)
CPU Caches:
L1 Data 32 KiB
L1 Instruction 32 KiB
L2 Unified 256 KiB (x6)
L3 Unified 12288 KiB
-----------------------------------------------------------------------------
Benchmark Time CPU Iterations
-----------------------------------------------------------------------------
BM_KISSFFT/1024/min_time:5.000 8.23 us 8.22 us 720099
BM_KISSFFT/1331/min_time:5.000 84.3 us 84.3 us 83650
BM_KISSFFT/2048/min_time:5.000 21.5 us 21.5 us 324763
BM_KISSFFT/4096/min_time:5.000 38.7 us 38.6 us 184193
BM_KISSFFT/8192/min_time:5.000 105 us 105 us 61205
BM_KISSFFT/11200/min_time:5.000 315 us 315 us 22347
BM_KISSFFT/11202/min_time:5.000 39972 us 39964 us 174 (semiprime)
BM_KISSFFT/16384/min_time:5.000 212 us 212 us 32645
BM_FFTW3_DOUBLE/1024/min_time:5.000 2.14 us 2.11 us 3350390
BM_FFTW3_DOUBLE/1331/min_time:5.000 7.96 us 7.95 us 831896
BM_FFTW3_DOUBLE/2048/min_time:5.000 4.54 us 4.53 us 1489583
BM_FFTW3_DOUBLE/4096/min_time:5.000 10.7 us 10.7 us 630747
BM_FFTW3_DOUBLE/8192/min_time:5.000 26.6 us 26.6 us 248424
BM_FFTW3_DOUBLE/11200/min_time:5.000 42.3 us 42.3 us 167924
BM_FFTW3_DOUBLE/11202/min_time:5.000 261 us 261 us 27397
BM_FFTW3_DOUBLE/16384/min_time:5.000 63.8 us 63.8 us 108027
BM_FFT_DSPLIB/1024/min_time:5.000 7.22 us 7.21 us 973596
BM_FFT_DSPLIB/1331/min_time:5.000 62.0 us 61.9 us 112298
BM_FFT_DSPLIB/2048/min_time:5.000 15.1 us 15.1 us 470951
BM_FFT_DSPLIB/4096/min_time:5.000 33.2 us 33.2 us 207217
BM_FFT_DSPLIB/8192/min_time:5.000 75.4 us 75.4 us 93323
BM_FFT_DSPLIB/11200/min_time:5.000 768 us 767 us 8979
BM_FFT_DSPLIB/11202/min_time:5.000 806 us 805 us 8380 (czt)
BM_FFT_DSPLIB/16384/min_time:5.000 171 us 171 us 40601
- Select FFT backend type (fftw/ne10)
- Add matrix syntax support
- Add custom allocator for
base_array<T>type - Add audioread/audiowrite functions (optional libsndfile?)
- Add chain syntax like
fft(x)->abs2()->pow2db() - Use
const_span<T>args
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent