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| 1 | /* | ||
| 2 | ============================================================================== | ||
| 3 | |||
| 4 | Yin.c | ||
| 5 | Created: 17 Jan 2017 1:20:23pm | ||
| 6 | Author: Michael R Mulshine | ||
| 7 | |||
| 8 | ============================================================================== | ||
| 9 | */ | ||
| 10 | |||
| 11 | #include "Yin.h" | ||
| 12 | |||
| 13 | #include <stdint.h> /* For standard interger types (int16_t) */ | ||
| 14 | #include <stdlib.h> /* For call to malloc */ | ||
| 15 | |||
| 16 | /* ------------------------------------------------------------------------------------------ | ||
| 17 | --------------------------------------------------------------------------- PRIVATE FUNCTIONS | ||
| 18 | -------------------------------------------------------------------------------------------*/ | ||
| 19 | |||
| 20 | /** | ||
| 21 | * Step 1: Calculates the squared difference of the signal with a shifted version of itself. | ||
| 22 | * @param buffer Buffer of samples to process. | ||
| 23 | * | ||
| 24 | * This is the Yin algorithms tweak on autocorellation. Read http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf | ||
| 25 | * for more details on what is in here and why it's done this way. | ||
| 26 | */ | ||
| 27 | void Yin_difference(Yin *yin, int16_t* buffer){ | ||
| 28 | int16_t i; | ||
| 29 | int16_t tau; | ||
| 30 | float delta; | ||
| 31 | |||
| 32 | /* Calculate the difference for difference shift values (tau) for the half of the samples */ | ||
| 33 | for(tau = 0 ; tau < yin->halfBufferSize; tau++){ | ||
| 34 | |||
| 35 | /* Take the difference of the signal with a shifted version of itself, then square it. | ||
| 36 | * (This is the Yin algorithm's tweak on autocorellation) */ | ||
| 37 | for(i = 0; i < yin->halfBufferSize; i++){ | ||
| 38 | delta = buffer[i] - buffer[i + tau]; | ||
| 39 | yin->yinBuffer[tau] += delta * delta; | ||
| 40 | } | ||
| 41 | } | ||
| 42 | } | ||
| 43 | |||
| 44 | |||
| 45 | /** | ||
| 46 | * Step 2: Calculate the cumulative mean on the normalised difference calculated in step 1 | ||
| 47 | * @param yin #Yin structure with information about the signal | ||
| 48 | * | ||
| 49 | * This goes through the Yin autocorellation values and finds out roughly where shift is which | ||
| 50 | * produced the smallest difference | ||
| 51 | */ | ||
| 52 | void Yin_cumulativeMeanNormalizedDifference(Yin *yin){ | ||
| 53 | int16_t tau; | ||
| 54 | float runningSum = 0; | ||
| 55 | yin->yinBuffer[0] = 1; | ||
| 56 | |||
| 57 | /* Sum all the values in the autocorellation buffer and nomalise the result, replacing | ||
| 58 | * the value in the autocorellation buffer with a cumulative mean of the normalised difference */ | ||
| 59 | for (tau = 1; tau < yin->halfBufferSize; tau++) { | ||
| 60 | runningSum += yin->yinBuffer[tau]; | ||
| 61 | yin->yinBuffer[tau] *= tau / runningSum; | ||
| 62 | } | ||
| 63 | } | ||
| 64 | |||
| 65 | /** | ||
| 66 | * Step 3: Search through the normalised cumulative mean array and find values that are over the threshold | ||
| 67 | * @return Shift (tau) which caused the best approximate autocorellation. -1 if no suitable value is found over the threshold. | ||
| 68 | */ | ||
| 69 | int16_t Yin_absoluteThreshold(Yin *yin){ | ||
| 70 | int16_t tau; | ||
| 71 | |||
| 72 | /* Search through the array of cumulative mean values, and look for ones that are over the threshold | ||
| 73 | * The first two positions in yinBuffer are always so start at the third (index 2) */ | ||
| 74 | for (tau = 2; tau < yin->halfBufferSize ; tau++) { | ||
| 75 | if (yin->yinBuffer[tau] < yin->threshold) { | ||
| 76 | while (tau + 1 < yin->halfBufferSize && yin->yinBuffer[tau + 1] < yin->yinBuffer[tau]) { | ||
| 77 | tau++; | ||
| 78 | } | ||
| 79 | /* found tau, exit loop and return | ||
| 80 | * store the probability | ||
| 81 | * From the YIN paper: The yin->threshold determines the list of | ||
| 82 | * candidates admitted to the set, and can be interpreted as the | ||
| 83 | * proportion of aperiodic power tolerated | ||
| 84 | * within a periodic signal. | ||
| 85 | * | ||
| 86 | * Since we want the periodicity and and not aperiodicity: | ||
| 87 | * periodicity = 1 - aperiodicity */ | ||
| 88 | yin->probability = 1 - yin->yinBuffer[tau]; | ||
| 89 | break; | ||
| 90 | } | ||
| 91 | } | ||
| 92 | |||
| 93 | /* if no pitch found, tau => -1 */ | ||
| 94 | if (tau == yin->halfBufferSize || yin->yinBuffer[tau] >= yin->threshold) { | ||
| 95 | tau = -1; | ||
| 96 | yin->probability = 0; | ||
| 97 | } | ||
| 98 | |||
| 99 | return tau; | ||
| 100 | } | ||
| 101 | |||
| 102 | /** | ||
| 103 | * Step 5: Interpolate the shift value (tau) to improve the pitch estimate. | ||
| 104 | * @param yin [description] | ||
| 105 | * @param tauEstimate [description] | ||
| 106 | * @return [description] | ||
| 107 | * | ||
| 108 | * The 'best' shift value for autocorellation is most likely not an interger shift of the signal. | ||
| 109 | * As we only autocorellated using integer shifts we should check that there isn't a better fractional | ||
| 110 | * shift value. | ||
| 111 | */ | ||
| 112 | float Yin_parabolicInterpolation(Yin *yin, int16_t tauEstimate) { | ||
| 113 | float betterTau; | ||
| 114 | int16_t x0; | ||
| 115 | int16_t x2; | ||
| 116 | |||
| 117 | /* Calculate the first polynomial coeffcient based on the current estimate of tau */ | ||
| 118 | if (tauEstimate < 1) { | ||
| 119 | x0 = tauEstimate; | ||
| 120 | } | ||
| 121 | else { | ||
| 122 | x0 = tauEstimate - 1; | ||
| 123 | } | ||
| 124 | |||
| 125 | /* Calculate the second polynomial coeffcient based on the current estimate of tau */ | ||
| 126 | if (tauEstimate + 1 < yin->halfBufferSize) { | ||
| 127 | x2 = tauEstimate + 1; | ||
| 128 | } | ||
| 129 | else { | ||
| 130 | x2 = tauEstimate; | ||
| 131 | } | ||
| 132 | |||
| 133 | /* Algorithm to parabolically interpolate the shift value tau to find a better estimate */ | ||
| 134 | if (x0 == tauEstimate) { | ||
| 135 | if (yin->yinBuffer[tauEstimate] <= yin->yinBuffer[x2]) { | ||
| 136 | betterTau = tauEstimate; | ||
| 137 | } | ||
| 138 | else { | ||
| 139 | betterTau = x2; | ||
| 140 | } | ||
| 141 | } | ||
| 142 | else if (x2 == tauEstimate) { | ||
| 143 | if (yin->yinBuffer[tauEstimate] <= yin->yinBuffer[x0]) { | ||
| 144 | betterTau = tauEstimate; | ||
| 145 | } | ||
| 146 | else { | ||
| 147 | betterTau = x0; | ||
| 148 | } | ||
| 149 | } | ||
| 150 | else { | ||
| 151 | float s0, s1, s2; | ||
| 152 | s0 = yin->yinBuffer[x0]; | ||
| 153 | s1 = yin->yinBuffer[tauEstimate]; | ||
| 154 | s2 = yin->yinBuffer[x2]; | ||
| 155 | // fixed AUBIO implementation, thanks to Karl Helgason: | ||
| 156 | // (2.0f * s1 - s2 - s0) was incorrectly multiplied with -1 | ||
| 157 | betterTau = tauEstimate + (s2 - s0) / (2 * (2 * s1 - s2 - s0)); | ||
| 158 | } | ||
| 159 | |||
| 160 | |||
| 161 | return betterTau; | ||
| 162 | } | ||
| 163 | |||
| 164 | |||
| 165 | |||
| 166 | |||
| 167 | |||
| 168 | /* ------------------------------------------------------------------------------------------ | ||
| 169 | ---------------------------------------------------------------------------- PUBLIC FUNCTIONS | ||
| 170 | -------------------------------------------------------------------------------------------*/ | ||
| 171 | |||
| 172 | |||
| 173 | |||
| 174 | /** | ||
| 175 | * Initialise the Yin pitch detection object | ||
| 176 | * @param yin Yin pitch detection object to initialise | ||
| 177 | * @param bufferSize Length of the audio buffer to analyse | ||
| 178 | * @param threshold Allowed uncertainty (e.g 0.05 will return a pitch with ~95% probability) | ||
| 179 | */ | ||
| 180 | void Yin_init(Yin *yin, int16_t bufferSize, float threshold){ | ||
| 181 | /* Initialise the fields of the Yin structure passed in */ | ||
| 182 | yin->bufferSize = bufferSize; | ||
| 183 | yin->halfBufferSize = bufferSize / 2; | ||
| 184 | yin->probability = 0.0; | ||
| 185 | yin->threshold = threshold; | ||
| 186 | |||
| 187 | /* Allocate the autocorellation buffer and initialise it to zero */ | ||
| 188 | yin->yinBuffer = (float *) malloc(sizeof(float)* yin->halfBufferSize); | ||
| 189 | |||
| 190 | int16_t i; | ||
| 191 | for(i = 0; i < yin->halfBufferSize; i++){ | ||
| 192 | yin->yinBuffer[i] = 0; | ||
| 193 | } | ||
| 194 | } | ||
| 195 | |||
| 196 | /** | ||
| 197 | * Runs the Yin pitch detection algortihm | ||
| 198 | * @param yin Initialised Yin object | ||
| 199 | * @param buffer Buffer of samples to analyse | ||
| 200 | * @return Fundamental frequency of the signal in Hz. Returns -1 if pitch can't be found | ||
| 201 | */ | ||
| 202 | float Yin_getPitch(Yin *yin, int16_t* buffer){ | ||
| 203 | int16_t tauEstimate = -1; | ||
| 204 | float pitchInHertz = -1; | ||
| 205 | |||
| 206 | /* Step 1: Calculates the squared difference of the signal with a shifted version of itself. */ | ||
| 207 | Yin_difference(yin, buffer); | ||
| 208 | |||
| 209 | /* Step 2: Calculate the cumulative mean on the normalised difference calculated in step 1 */ | ||
| 210 | Yin_cumulativeMeanNormalizedDifference(yin); | ||
| 211 | |||
| 212 | /* Step 3: Search through the normalised cumulative mean array and find values that are over the threshold */ | ||
| 213 | tauEstimate = Yin_absoluteThreshold(yin); | ||
| 214 | |||
| 215 | /* Step 5: Interpolate the shift value (tau) to improve the pitch estimate. */ | ||
| 216 | if(tauEstimate != -1){ | ||
| 217 | pitchInHertz = YIN_SAMPLING_RATE / Yin_parabolicInterpolation(yin, tauEstimate); | ||
| 218 | } | ||
| 219 | |||
| 220 | return pitchInHertz; | ||
| 221 | } | ||
| 222 | |||
| 223 | /** | ||
| 224 | * Certainty of the pitch found | ||
| 225 | * @param yin Yin object that has been run over a buffer | ||
| 226 | * @return Returns the certainty of the note found as a decimal (i.e 0.3 is 30%) | ||
| 227 | */ | ||
| 228 | float Yin_getProbability(Yin *yin){ | ||
| 229 | return yin->probability; | ||
| 230 | } | ||
| 231 | |||
| 232 | |||
| 233 | |||