android / platform / frameworks / av / c570778430a22b5488cae72982cf9fb8033dbda3 / . / services / audioflinger / AudioResamplerFirGen.h

/* | |

* Copyright (C) 2013 The Android Open Source Project | |

* | |

* Licensed under the Apache License, Version 2.0 (the "License"); | |

* you may not use this file except in compliance with the License. | |

* You may obtain a copy of the License at | |

* | |

* http://www.apache.org/licenses/LICENSE-2.0 | |

* | |

* Unless required by applicable law or agreed to in writing, software | |

* distributed under the License is distributed on an "AS IS" BASIS, | |

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |

* See the License for the specific language governing permissions and | |

* limitations under the License. | |

*/ | |

#ifndef ANDROID_AUDIO_RESAMPLER_FIR_GEN_H | |

#define ANDROID_AUDIO_RESAMPLER_FIR_GEN_H | |

namespace android { | |

/* | |

* generates a sine wave at equal steps. | |

* | |

* As most of our functions use sine or cosine at equal steps, | |

* it is very efficient to compute them that way (single multiply and subtract), | |

* rather than invoking the math library sin() or cos() each time. | |

* | |

* SineGen uses Goertzel's Algorithm (as a generator not a filter) | |

* to calculate sine(wstart + n * wstep) or cosine(wstart + n * wstep) | |

* by stepping through 0, 1, ... n. | |

* | |

* e^i(wstart+wstep) = 2cos(wstep) * e^i(wstart) - e^i(wstart-wstep) | |

* | |

* or looking at just the imaginary sine term, as the cosine follows identically: | |

* | |

* sin(wstart+wstep) = 2cos(wstep) * sin(wstart) - sin(wstart-wstep) | |

* | |

* Goertzel's algorithm is more efficient than the angle addition formula, | |

* e^i(wstart+wstep) = e^i(wstart) * e^i(wstep), which takes up to | |

* 4 multiplies and 2 adds (or 3* and 3+) and requires both sine and | |

* cosine generation due to the complex * complex multiply (full rotation). | |

* | |

* See: http://en.wikipedia.org/wiki/Goertzel_algorithm | |

* | |

*/ | |

class SineGen { | |

public: | |

SineGen(double wstart, double wstep, bool cosine = false) { | |

if (cosine) { | |

mCurrent = cos(wstart); | |

mPrevious = cos(wstart - wstep); | |

} else { | |

mCurrent = sin(wstart); | |

mPrevious = sin(wstart - wstep); | |

} | |

mTwoCos = 2.*cos(wstep); | |

} | |

SineGen(double expNow, double expPrev, double twoCosStep) { | |

mCurrent = expNow; | |

mPrevious = expPrev; | |

mTwoCos = twoCosStep; | |

} | |

inline double value() const { | |

return mCurrent; | |

} | |

inline void advance() { | |

double tmp = mCurrent; | |

mCurrent = mCurrent*mTwoCos - mPrevious; | |

mPrevious = tmp; | |

} | |

inline double valueAdvance() { | |

double tmp = mCurrent; | |

mCurrent = mCurrent*mTwoCos - mPrevious; | |

mPrevious = tmp; | |

return tmp; | |

} | |

private: | |

double mCurrent; // current value of sine/cosine | |

double mPrevious; // previous value of sine/cosine | |

double mTwoCos; // stepping factor | |

}; | |

/* | |

* generates a series of sine generators, phase offset by fixed steps. | |

* | |

* This is used to generate polyphase sine generators, one per polyphase | |

* in the filter code below. | |

* | |

* The SineGen returned by value() starts at innerStart = outerStart + n*outerStep; | |

* increments by innerStep. | |

* | |

*/ | |

class SineGenGen { | |

public: | |

SineGenGen(double outerStart, double outerStep, double innerStep, bool cosine = false) | |

: mSineInnerCur(outerStart, outerStep, cosine), | |

mSineInnerPrev(outerStart-innerStep, outerStep, cosine) | |

{ | |

mTwoCos = 2.*cos(innerStep); | |

} | |

inline SineGen value() { | |

return SineGen(mSineInnerCur.value(), mSineInnerPrev.value(), mTwoCos); | |

} | |

inline void advance() { | |

mSineInnerCur.advance(); | |

mSineInnerPrev.advance(); | |

} | |

inline SineGen valueAdvance() { | |

return SineGen(mSineInnerCur.valueAdvance(), mSineInnerPrev.valueAdvance(), mTwoCos); | |

} | |

private: | |

SineGen mSineInnerCur; // generate the inner sine values (stepped by outerStep). | |

SineGen mSineInnerPrev; // generate the inner sine previous values | |

// (behind by innerStep, stepped by outerStep). | |

double mTwoCos; // the inner stepping factor for the returned SineGen. | |

}; | |

static inline double sqr(double x) { | |

return x * x; | |

} | |

/* | |

* rounds a double to the nearest integer for FIR coefficients. | |

* | |

* One variant uses noise shaping, which must keep error history | |

* to work (the err parameter, initialized to 0). | |

* The other variant is a non-noise shaped version for | |

* S32 coefficients (noise shaping doesn't gain much). | |

* | |

* Caution: No bounds saturation is applied, but isn't needed in this case. | |

* | |

* @param x is the value to round. | |

* | |

* @param maxval is the maximum integer scale factor expressed as an int64 (for headroom). | |

* Typically this may be the maximum positive integer+1 (using the fact that double precision | |

* FIR coefficients generated here are never that close to 1.0 to pose an overflow condition). | |

* | |

* @param err is the previous error (actual - rounded) for the previous rounding op. | |

* For 16b coefficients this can improve stopband dB performance by up to 2dB. | |

* | |

* Many variants exist for the noise shaping: http://en.wikipedia.org/wiki/Noise_shaping | |

* | |

*/ | |

static inline int64_t toint(double x, int64_t maxval, double& err) { | |

double val = x * maxval; | |

double ival = floor(val + 0.5 + err*0.2); | |

err = val - ival; | |

return static_cast<int64_t>(ival); | |

} | |

static inline int64_t toint(double x, int64_t maxval) { | |

return static_cast<int64_t>(floor(x * maxval + 0.5)); | |

} | |

/* | |

* Modified Bessel function of the first kind | |

* http://en.wikipedia.org/wiki/Bessel_function | |

* | |

* The formulas are taken from Abramowitz and Stegun, | |

* _Handbook of Mathematical Functions_ (links below): | |

* | |

* http://people.math.sfu.ca/~cbm/aands/page_375.htm | |

* http://people.math.sfu.ca/~cbm/aands/page_378.htm | |

* | |

* http://dlmf.nist.gov/10.25 | |

* http://dlmf.nist.gov/10.40 | |

* | |

* Note we assume x is nonnegative (the function is symmetric, | |

* pass in the absolute value as needed). | |

* | |

* Constants are compile time derived with templates I0Term<> and | |

* I0ATerm<> to the precision of the compiler. The series can be expanded | |

* to any precision needed, but currently set around 24b precision. | |

* | |

* We use a bit of template math here, constexpr would probably be | |

* more appropriate for a C++11 compiler. | |

* | |

* For the intermediate range 3.75 < x < 15, we use minimax polynomial fit. | |

* | |

*/ | |

template <int N> | |

struct I0Term { | |

static const double value = I0Term<N-1>::value / (4. * N * N); | |

}; | |

template <> | |

struct I0Term<0> { | |

static const double value = 1.; | |

}; | |

template <int N> | |

struct I0ATerm { | |

static const double value = I0ATerm<N-1>::value * (2.*N-1.) * (2.*N-1.) / (8. * N); | |

}; | |

template <> | |

struct I0ATerm<0> { // 1/sqrt(2*PI); | |

static const double value = 0.398942280401432677939946059934381868475858631164934657665925; | |

}; | |

#if USE_HORNERS_METHOD | |

/* Polynomial evaluation of A + Bx + Cx^2 + Dx^3 + ... | |

* using Horner's Method: http://en.wikipedia.org/wiki/Horner's_method | |

* | |

* This has fewer multiplications than Estrin's method below, but has back to back | |

* floating point dependencies. | |

* | |

* On ARM this appears to work slower, so USE_HORNERS_METHOD is not default enabled. | |

*/ | |

inline double Poly2(double A, double B, double x) { | |

return A + x * B; | |

} | |

inline double Poly4(double A, double B, double C, double D, double x) { | |

return A + x * (B + x * (C + x * (D))); | |

} | |

inline double Poly7(double A, double B, double C, double D, double E, double F, double G, | |

double x) { | |

return A + x * (B + x * (C + x * (D + x * (E + x * (F + x * (G)))))); | |

} | |

inline double Poly9(double A, double B, double C, double D, double E, double F, double G, | |

double H, double I, double x) { | |

return A + x * (B + x * (C + x * (D + x * (E + x * (F + x * (G + x * (H + x * (I)))))))); | |

} | |

#else | |

/* Polynomial evaluation of A + Bx + Cx^2 + Dx^3 + ... | |

* using Estrin's Method: http://en.wikipedia.org/wiki/Estrin's_scheme | |

* | |

* This is typically faster, perhaps gains about 5-10% overall on ARM processors | |

* over Horner's method above. | |

*/ | |

inline double Poly2(double A, double B, double x) { | |

return A + B * x; | |

} | |

inline double Poly3(double A, double B, double C, double x, double x2) { | |

return Poly2(A, B, x) + C * x2; | |

} | |

inline double Poly3(double A, double B, double C, double x) { | |

return Poly2(A, B, x) + C * x * x; | |

} | |

inline double Poly4(double A, double B, double C, double D, double x, double x2) { | |

return Poly2(A, B, x) + Poly2(C, D, x) * x2; // same as poly2(poly2, poly2, x2); | |

} | |

inline double Poly4(double A, double B, double C, double D, double x) { | |

return Poly4(A, B, C, D, x, x * x); | |

} | |

inline double Poly7(double A, double B, double C, double D, double E, double F, double G, | |

double x) { | |

double x2 = x * x; | |

return Poly4(A, B, C, D, x, x2) + Poly3(E, F, G, x, x2) * (x2 * x2); | |

} | |

inline double Poly8(double A, double B, double C, double D, double E, double F, double G, | |

double H, double x, double x2, double x4) { | |

return Poly4(A, B, C, D, x, x2) + Poly4(E, F, G, H, x, x2) * x4; | |

} | |

inline double Poly9(double A, double B, double C, double D, double E, double F, double G, | |

double H, double I, double x) { | |

double x2 = x * x; | |

#if 1 | |

// It does not seem faster to explicitly decompose Poly8 into Poly4, but | |

// could depend on compiler floating point scheduling. | |

double x4 = x2 * x2; | |

return Poly8(A, B, C, D, E, F, G, H, x, x2, x4) + I * (x4 * x4); | |

#else | |

double val = Poly4(A, B, C, D, x, x2); | |

double x4 = x2 * x2; | |

return val + Poly4(E, F, G, H, x, x2) * x4 + I * (x4 * x4); | |

#endif | |

} | |

#endif | |

static inline double I0(double x) { | |

if (x < 3.75) { | |

x *= x; | |

return Poly7(I0Term<0>::value, I0Term<1>::value, | |

I0Term<2>::value, I0Term<3>::value, | |

I0Term<4>::value, I0Term<5>::value, | |

I0Term<6>::value, x); // e < 1.6e-7 | |

} | |

if (1) { | |

/* | |

* Series expansion coefs are easy to calculate, but are expanded around 0, | |

* so error is unequal over the interval 0 < x < 3.75, the error being | |

* significantly better near 0. | |

* | |

* A better solution is to use precise minimax polynomial fits. | |

* | |

* We use a slightly more complicated solution for 3.75 < x < 15, based on | |

* the tables in Blair and Edwards, "Stable Rational Minimax Approximations | |

* to the Modified Bessel Functions I0(x) and I1(x)", Chalk Hill Nuclear Laboratory, | |

* AECL-4928. | |

* | |

* http://www.iaea.org/inis/collection/NCLCollectionStore/_Public/06/178/6178667.pdf | |

* | |

* See Table 11 for 0 < x < 15; e < 10^(-7.13). | |

* | |

* Note: Beta cannot exceed 15 (hence Stopband cannot exceed 144dB = 24b). | |

* | |

* This speeds up overall computation by about 40% over using the else clause below, | |

* which requires sqrt and exp. | |

* | |

*/ | |

x *= x; | |

double num = Poly9(-0.13544938430e9, -0.33153754512e8, | |

-0.19406631946e7, -0.48058318783e5, | |

-0.63269783360e3, -0.49520779070e1, | |

-0.24970910370e-1, -0.74741159550e-4, | |

-0.18257612460e-6, x); | |

double y = x - 225.; // reflection around 15 (squared) | |

double den = Poly4(-0.34598737196e8, 0.23852643181e6, | |

-0.70699387620e3, 0.10000000000e1, y); | |

return num / den; | |

#if IO_EXTENDED_BETA | |

/* Table 42 for x > 15; e < 10^(-8.11). | |

* This is used for Beta>15, but is disabled here as | |

* we never use Beta that high. | |

* | |

* NOTE: This should be enabled only for x > 15. | |

*/ | |

double y = 1./x; | |

double z = y - (1./15); | |

double num = Poly2(0.415079861746e1, -0.5149092496e1, z); | |

double den = Poly3(0.103150763823e2, -0.14181687413e2, | |

0.1000000000e1, z); | |

return exp(x) * sqrt(y) * num / den; | |

#endif | |

} else { | |

/* | |

* NOT USED, but reference for large Beta. | |

* | |

* Abramowitz and Stegun asymptotic formula. | |

* works for x > 3.75. | |

*/ | |

double y = 1./x; | |

return exp(x) * sqrt(y) * | |

// note: reciprocal squareroot may be easier! | |

// http://en.wikipedia.org/wiki/Fast_inverse_square_root | |

Poly9(I0ATerm<0>::value, I0ATerm<1>::value, | |

I0ATerm<2>::value, I0ATerm<3>::value, | |

I0ATerm<4>::value, I0ATerm<5>::value, | |

I0ATerm<6>::value, I0ATerm<7>::value, | |

I0ATerm<8>::value, y); // (... e) < 1.9e-7 | |

} | |

} | |

/* A speed optimized version of the Modified Bessel I0() which incorporates | |

* the sqrt and numerator multiply and denominator divide into the computation. | |

* This speeds up filter computation by about 10-15%. | |

*/ | |

static inline double I0SqrRat(double x2, double num, double den) { | |

if (x2 < (3.75 * 3.75)) { | |

return Poly7(I0Term<0>::value, I0Term<1>::value, | |

I0Term<2>::value, I0Term<3>::value, | |

I0Term<4>::value, I0Term<5>::value, | |

I0Term<6>::value, x2) * num / den; // e < 1.6e-7 | |

} | |

num *= Poly9(-0.13544938430e9, -0.33153754512e8, | |

-0.19406631946e7, -0.48058318783e5, | |

-0.63269783360e3, -0.49520779070e1, | |

-0.24970910370e-1, -0.74741159550e-4, | |

-0.18257612460e-6, x2); // e < 10^(-7.13). | |

double y = x2 - 225.; // reflection around 15 (squared) | |

den *= Poly4(-0.34598737196e8, 0.23852643181e6, | |

-0.70699387620e3, 0.10000000000e1, y); | |

return num / den; | |

} | |

/* | |

* calculates the transition bandwidth for a Kaiser filter | |

* | |

* Formula 3.2.8, Vaidyanathan, _Multirate Systems and Filter Banks_, p. 48 | |

* Formula 7.76, Oppenheim and Schafer, _Discrete-time Signal Processing, 3e_, p. 542 | |

* | |

* @param halfNumCoef is half the number of coefficients per filter phase. | |

* | |

* @param stopBandAtten is the stop band attenuation desired. | |

* | |

* @return the transition bandwidth in normalized frequency (0 <= f <= 0.5) | |

*/ | |

static inline double firKaiserTbw(int halfNumCoef, double stopBandAtten) { | |

return (stopBandAtten - 7.95)/((2.*14.36)*halfNumCoef); | |

} | |

/* | |

* calculates the fir transfer response of the overall polyphase filter at w. | |

* | |

* Calculates the DTFT transfer coefficient H(w) for 0 <= w <= PI, utilizing the | |

* fact that h[n] is symmetric (cosines only, no complex arithmetic). | |

* | |

* We use Goertzel's algorithm to accelerate the computation to essentially | |

* a single multiply and 2 adds per filter coefficient h[]. | |

* | |

* Be careful be careful to consider that h[n] is the overall polyphase filter, | |

* with L phases, so rescaling H(w)/L is probably what you expect for "unity gain", | |

* as you only use one of the polyphases at a time. | |

*/ | |

template <typename T> | |

static inline double firTransfer(const T* coef, int L, int halfNumCoef, double w) { | |

double accum = static_cast<double>(coef[0])*0.5; // "center coefficient" from first bank | |

coef += halfNumCoef; // skip first filterbank (picked up by the last filterbank). | |

#if SLOW_FIRTRANSFER | |

/* Original code for reference. This is equivalent to the code below, but slower. */ | |

for (int i=1 ; i<=L ; ++i) { | |

for (int j=0, ix=i ; j<halfNumCoef ; ++j, ix+=L) { | |

accum += cos(ix*w)*static_cast<double>(*coef++); | |

} | |

} | |

#else | |

/* | |

* Our overall filter is stored striped by polyphases, not a contiguous h[n]. | |

* We could fetch coefficients in a non-contiguous fashion | |

* but that will not scale to vector processing. | |

* | |

* We apply Goertzel's algorithm directly to each polyphase filter bank instead of | |

* using cosine generation/multiplication, thereby saving one multiply per inner loop. | |

* | |

* See: http://en.wikipedia.org/wiki/Goertzel_algorithm | |

* Also: Oppenheim and Schafer, _Discrete Time Signal Processing, 3e_, p. 720. | |

* | |

* We use the basic recursion to incorporate the cosine steps into real sequence x[n]: | |

* s[n] = x[n] + (2cosw)*s[n-1] + s[n-2] | |

* | |

* y[n] = s[n] - e^(iw)s[n-1] | |

* = sum_{k=-\infty}^{n} x[k]e^(-iw(n-k)) | |

* = e^(-iwn) sum_{k=0}^{n} x[k]e^(iwk) | |

* | |

* The summation contains the frequency steps we want multiplied by the source | |

* (similar to a DTFT). | |

* | |

* Using symmetry, and just the real part (be careful, this must happen | |

* after any internal complex multiplications), the polyphase filterbank | |

* transfer function is: | |

* | |

* Hpp[n, w, w_0] = sum_{k=0}^{n} x[k] * cos(wk + w_0) | |

* = Re{ e^(iwn + iw_0) y[n]} | |

* = cos(wn+w_0) * s[n] - cos(w(n+1)+w_0) * s[n-1] | |

* | |

* using the fact that s[n] of real x[n] is real. | |

* | |

*/ | |

double dcos = 2. * cos(L*w); | |

int start = ((halfNumCoef)*L + 1); | |

SineGen cc((start - L) * w, w, true); // cosine | |

SineGen cp(start * w, w, true); // cosine | |

for (int i=1 ; i<=L ; ++i) { | |

double sc = 0; | |

double sp = 0; | |

for (int j=0 ; j<halfNumCoef ; ++j) { | |

double tmp = sc; | |

sc = static_cast<double>(*coef++) + dcos*sc - sp; | |

sp = tmp; | |

} | |

// If we are awfully clever, we can apply Goertzel's algorithm | |

// again on the sc and sp sequences returned here. | |

accum += cc.valueAdvance() * sc - cp.valueAdvance() * sp; | |

} | |

#endif | |

return accum*2.; | |

} | |

/* | |

* evaluates the minimum and maximum |H(f)| bound in a band region. | |

* | |

* This is usually done with equally spaced increments in the target band in question. | |

* The passband is often very small, and sampled that way. The stopband is often much | |

* larger. | |

* | |

* We use the fact that the overall polyphase filter has an additional bank at the end | |

* for interpolation; hence it is overspecified for the H(f) computation. Thus the | |

* first polyphase is never actually checked, excepting its first term. | |

* | |

* In this code we use the firTransfer() evaluator above, which uses Goertzel's | |

* algorithm to calculate the transfer function at each point. | |

* | |

* TODO: An alternative with equal spacing is the FFT/DFT. An alternative with unequal | |

* spacing is a chirp transform. | |

* | |

* @param coef is the designed polyphase filter banks | |

* | |

* @param L is the number of phases (for interpolation) | |

* | |

* @param halfNumCoef should be half the number of coefficients for a single | |

* polyphase. | |

* | |

* @param fstart is the normalized frequency start. | |

* | |

* @param fend is the normalized frequency end. | |

* | |

* @param steps is the number of steps to take (sampling) between frequency start and end | |

* | |

* @param firMin returns the minimum transfer |H(f)| found | |

* | |

* @param firMax returns the maximum transfer |H(f)| found | |

* | |

* 0 <= f <= 0.5. | |

* This is used to test passband and stopband performance. | |

*/ | |

template <typename T> | |

static void testFir(const T* coef, int L, int halfNumCoef, | |

double fstart, double fend, int steps, double &firMin, double &firMax) { | |

double wstart = fstart*(2.*M_PI); | |

double wend = fend*(2.*M_PI); | |

double wstep = (wend - wstart)/steps; | |

double fmax, fmin; | |

double trf = firTransfer(coef, L, halfNumCoef, wstart); | |

if (trf<0) { | |

trf = -trf; | |

} | |

fmin = fmax = trf; | |

wstart += wstep; | |

for (int i=1; i<steps; ++i) { | |

trf = firTransfer(coef, L, halfNumCoef, wstart); | |

if (trf<0) { | |

trf = -trf; | |

} | |

if (trf>fmax) { | |

fmax = trf; | |

} | |

else if (trf<fmin) { | |

fmin = trf; | |

} | |

wstart += wstep; | |

} | |

// renormalize - this is only needed for integer filter types | |

double norm = 1./((1ULL<<(sizeof(T)*8-1))*L); | |

firMin = fmin * norm; | |

firMax = fmax * norm; | |

} | |

/* | |

* evaluates the |H(f)| lowpass band characteristics. | |

* | |

* This function tests the lowpass characteristics for the overall polyphase filter, | |

* and is used to verify the design. For this case, fp should be set to the | |

* passband normalized frequency from 0 to 0.5 for the overall filter (thus it | |

* is the designed polyphase bank value / L). Likewise for fs. | |

* | |

* @param coef is the designed polyphase filter banks | |

* | |

* @param L is the number of phases (for interpolation) | |

* | |

* @param halfNumCoef should be half the number of coefficients for a single | |

* polyphase. | |

* | |

* @param fp is the passband normalized frequency, 0 < fp < fs < 0.5. | |

* | |

* @param fs is the stopband normalized frequency, 0 < fp < fs < 0.5. | |

* | |

* @param passSteps is the number of passband sampling steps. | |

* | |

* @param stopSteps is the number of stopband sampling steps. | |

* | |

* @param passMin is the minimum value in the passband | |

* | |

* @param passMax is the maximum value in the passband (useful for scaling). This should | |

* be less than 1., to avoid sine wave test overflow. | |

* | |

* @param passRipple is the passband ripple. Typically this should be less than 0.1 for | |

* an audio filter. Generally speaker/headphone device characteristics will dominate | |

* the passband term. | |

* | |

* @param stopMax is the maximum value in the stopband. | |

* | |

* @param stopRipple is the stopband ripple, also known as stopband attenuation. | |

* Typically this should be greater than ~80dB for low quality, and greater than | |

* ~100dB for full 16b quality, otherwise aliasing may become noticeable. | |

* | |

*/ | |

template <typename T> | |

static void testFir(const T* coef, int L, int halfNumCoef, | |

double fp, double fs, int passSteps, int stopSteps, | |

double &passMin, double &passMax, double &passRipple, | |

double &stopMax, double &stopRipple) { | |

double fmin, fmax; | |

testFir(coef, L, halfNumCoef, 0., fp, passSteps, fmin, fmax); | |

double d1 = (fmax - fmin)/2.; | |

passMin = fmin; | |

passMax = fmax; | |

passRipple = -20.*log10(1. - d1); // passband ripple | |

testFir(coef, L, halfNumCoef, fs, 0.5, stopSteps, fmin, fmax); | |

// fmin is really not important for the stopband. | |

stopMax = fmax; | |

stopRipple = -20.*log10(fmax); // stopband ripple/attenuation | |

} | |

/* | |

* Calculates the overall polyphase filter based on a windowed sinc function. | |

* | |

* The windowed sinc is an odd length symmetric filter of exactly L*halfNumCoef*2+1 | |

* taps for the entire kernel. This is then decomposed into L+1 polyphase filterbanks. | |

* The last filterbank is used for interpolation purposes (and is mostly composed | |

* of the first bank shifted by one sample), and is unnecessary if one does | |

* not do interpolation. | |

* | |

* We use the last filterbank for some transfer function calculation purposes, | |

* so it needs to be generated anyways. | |

* | |

* @param coef is the caller allocated space for coefficients. This should be | |

* exactly (L+1)*halfNumCoef in size. | |

* | |

* @param L is the number of phases (for interpolation) | |

* | |

* @param halfNumCoef should be half the number of coefficients for a single | |

* polyphase. | |

* | |

* @param stopBandAtten is the stopband value, should be >50dB. | |

* | |

* @param fcr is cutoff frequency/sampling rate (<0.5). At this point, the energy | |

* should be 6dB less. (fcr is where the amplitude drops by half). Use the | |

* firKaiserTbw() to calculate the transition bandwidth. fcr is the midpoint | |

* between the stop band and the pass band (fstop+fpass)/2. | |

* | |

* @param atten is the attenuation (generally slightly less than 1). | |

*/ | |

template <typename T> | |

static inline void firKaiserGen(T* coef, int L, int halfNumCoef, | |

double stopBandAtten, double fcr, double atten) { | |

// | |

// Formula 3.2.5, 3.2.7, Vaidyanathan, _Multirate Systems and Filter Banks_, p. 48 | |

// Formula 7.75, Oppenheim and Schafer, _Discrete-time Signal Processing, 3e_, p. 542 | |

// | |

// See also: http://melodi.ee.washington.edu/courses/ee518/notes/lec17.pdf | |

// | |

// Kaiser window and beta parameter | |

// | |

// | 0.1102*(A - 8.7) A > 50 | |

// beta = | 0.5842*(A - 21)^0.4 + 0.07886*(A - 21) 21 <= A <= 50 | |

// | 0. A < 21 | |

// | |

// with A is the desired stop-band attenuation in dBFS | |

// | |

// 30 dB 2.210 | |

// 40 dB 3.384 | |

// 50 dB 4.538 | |

// 60 dB 5.658 | |

// 70 dB 6.764 | |

// 80 dB 7.865 | |

// 90 dB 8.960 | |

// 100 dB 10.056 | |

const int N = L * halfNumCoef; // non-negative half | |

const double beta = 0.1102 * (stopBandAtten - 8.7); // >= 50dB always | |

const double xstep = (2. * M_PI) * fcr / L; | |

const double xfrac = 1. / N; | |

const double yscale = atten * L / (I0(beta) * M_PI); | |

const double sqrbeta = sqr(beta); | |

// We use sine generators, which computes sines on regular step intervals. | |

// This speeds up overall computation about 40% from computing the sine directly. | |

SineGenGen sgg(0., xstep, L*xstep); // generates sine generators (one per polyphase) | |

for (int i=0 ; i<=L ; ++i) { // generate an extra set of coefs for interpolation | |

// computation for a single polyphase of the overall filter. | |

SineGen sg = sgg.valueAdvance(); // current sine generator for "j" inner loop. | |

double err = 0; // for noise shaping on int16_t coefficients (over each polyphase) | |

for (int j=0, ix=i ; j<halfNumCoef ; ++j, ix+=L) { | |

double y; | |

if (CC_LIKELY(ix)) { | |

double x = static_cast<double>(ix); | |

// sine generator: sg.valueAdvance() returns sin(ix*xstep); | |

// y = I0(beta * sqrt(1.0 - sqr(x * xfrac))) * yscale * sg.valueAdvance() / x; | |

y = I0SqrRat(sqrbeta * (1.0 - sqr(x * xfrac)), yscale * sg.valueAdvance(), x); | |

} else { | |

y = 2. * atten * fcr; // center of filter, sinc(0) = 1. | |

sg.advance(); | |

} | |

if (is_same<T, int16_t>::value) { // int16_t needs noise shaping | |

*coef++ = static_cast<T>(toint(y, 1ULL<<(sizeof(T)*8-1), err)); | |

} else if (is_same<T, int32_t>::value) { | |

*coef++ = static_cast<T>(toint(y, 1ULL<<(sizeof(T)*8-1))); | |

} else { // assumed float or double | |

*coef++ = static_cast<T>(y); | |

} | |

} | |

} | |

} | |

}; // namespace android | |

#endif /*ANDROID_AUDIO_RESAMPLER_FIR_GEN_H*/ |