|
| 1 | +use std::f64; |
| 2 | + |
| 3 | +#[derive(Clone, Debug)] |
| 4 | +pub struct YinResult { |
| 5 | + sample_rate: f64, |
| 6 | + best_lag: usize, |
| 7 | + cmndf: Vec<f64>, |
| 8 | +} |
| 9 | + |
| 10 | +impl YinResult { |
| 11 | + pub fn get_frequency(&self) -> f64 { |
| 12 | + self.sample_rate / self.best_lag as f64 |
| 13 | + } |
| 14 | + |
| 15 | + pub fn get_frequency_with_interpolation(&self) -> f64 { |
| 16 | + let best_lag_with_interpolation = parabolic_interpolation(self.best_lag, &self.cmndf); |
| 17 | + self.sample_rate / best_lag_with_interpolation |
| 18 | + } |
| 19 | +} |
| 20 | + |
| 21 | +fn parabolic_interpolation(lag: usize, cmndf: &[f64]) -> f64 { |
| 22 | + let x0 = lag.saturating_sub(1); // max(0, lag-1) |
| 23 | + let x2 = usize::min(cmndf.len() - 1, lag + 1); |
| 24 | + let s0 = cmndf[x0]; |
| 25 | + let s1 = cmndf[lag]; |
| 26 | + let s2 = cmndf[x2]; |
| 27 | + let denom = s0 - 2.0 * s1 + s2; |
| 28 | + if denom == 0.0 { |
| 29 | + return lag as f64; |
| 30 | + } |
| 31 | + let delta = (s0 - s2) / (2.0 * denom); |
| 32 | + lag as f64 + delta |
| 33 | +} |
| 34 | + |
| 35 | +#[derive(Clone, Debug)] |
| 36 | +pub struct Yin { |
| 37 | + threshold: f64, |
| 38 | + min_lag: usize, |
| 39 | + max_lag: usize, |
| 40 | + sample_rate: f64, |
| 41 | +} |
| 42 | + |
| 43 | +impl Yin { |
| 44 | + pub fn init( |
| 45 | + threshold: f64, |
| 46 | + min_expected_frequency: f64, |
| 47 | + max_expected_frequency: f64, |
| 48 | + sample_rate: f64, |
| 49 | + ) -> Yin { |
| 50 | + let min_lag = (sample_rate / max_expected_frequency) as usize; |
| 51 | + let max_lag = (sample_rate / min_expected_frequency) as usize; |
| 52 | + Yin { |
| 53 | + threshold, |
| 54 | + min_lag, |
| 55 | + max_lag, |
| 56 | + sample_rate, |
| 57 | + } |
| 58 | + } |
| 59 | + |
| 60 | + pub fn yin(&self, frequencies: &[f64]) -> Result<YinResult, String> { |
| 61 | + let df = difference_function_values(frequencies, self.max_lag); |
| 62 | + let cmndf = cumulative_mean_normalized_difference_function(&df, self.max_lag); |
| 63 | + let best_lag = find_cmndf_argmin(&cmndf, self.min_lag, self.max_lag, self.threshold); |
| 64 | + match best_lag { |
| 65 | + _ if best_lag == 0 => Err(format!( |
| 66 | + "Could not find lag value which minimizes CMNDF below the given threshold {}", |
| 67 | + self.threshold |
| 68 | + )), |
| 69 | + _ => Ok(YinResult { |
| 70 | + sample_rate: self.sample_rate, |
| 71 | + best_lag, |
| 72 | + cmndf, |
| 73 | + }), |
| 74 | + } |
| 75 | + } |
| 76 | +} |
| 77 | + |
| 78 | +#[allow(clippy::needless_range_loop)] |
| 79 | +fn difference_function_values(frequencies: &[f64], max_lag: usize) -> Vec<f64> { |
| 80 | + let mut df_list = vec![0.0; max_lag + 1]; |
| 81 | + for lag in 1..=max_lag { |
| 82 | + df_list[lag] = difference_function(frequencies, lag); |
| 83 | + } |
| 84 | + df_list |
| 85 | +} |
| 86 | + |
| 87 | +fn difference_function(f: &[f64], lag: usize) -> f64 { |
| 88 | + let mut sum = 0.0; |
| 89 | + let n = f.len(); |
| 90 | + for i in 0..(n - lag) { |
| 91 | + let diff = f[i] - f[i + lag]; |
| 92 | + sum += diff * diff; |
| 93 | + } |
| 94 | + sum |
| 95 | +} |
| 96 | + |
| 97 | +const EPSILON: f64 = 1e-10; |
| 98 | +fn cumulative_mean_normalized_difference_function(df: &[f64], max_lag: usize) -> Vec<f64> { |
| 99 | + let mut cmndf = vec![0.0; max_lag + 1]; |
| 100 | + cmndf[0] = 1.0; |
| 101 | + let mut sum = 0.0; |
| 102 | + for lag in 1..=max_lag { |
| 103 | + sum += df[lag]; |
| 104 | + cmndf[lag] = lag as f64 * df[lag] / if sum == 0.0 { EPSILON } else { sum }; |
| 105 | + } |
| 106 | + cmndf |
| 107 | +} |
| 108 | + |
| 109 | +fn find_cmndf_argmin(cmndf: &[f64], min_lag: usize, max_lag: usize, threshold: f64) -> usize { |
| 110 | + let mut lag = min_lag; |
| 111 | + while lag <= max_lag { |
| 112 | + if cmndf[lag] < threshold { |
| 113 | + while lag < max_lag && cmndf[lag + 1] < cmndf[lag] { |
| 114 | + lag += 1; |
| 115 | + } |
| 116 | + return lag; |
| 117 | + } |
| 118 | + lag += 1; |
| 119 | + } |
| 120 | + 0 |
| 121 | +} |
| 122 | + |
| 123 | +#[cfg(test)] |
| 124 | +mod tests { |
| 125 | + use super::*; |
| 126 | + |
| 127 | + fn generate_sine_wave(frequency: f64, sample_rate: f64, duration_secs: f64) -> Vec<f64> { |
| 128 | + let total_samples = (sample_rate * duration_secs).round() as usize; |
| 129 | + let two_pi_f = 2.0 * std::f64::consts::PI * frequency; |
| 130 | + |
| 131 | + (0..total_samples) |
| 132 | + .map(|n| { |
| 133 | + let t = n as f64 / sample_rate; |
| 134 | + (two_pi_f * t).sin() |
| 135 | + }) |
| 136 | + .collect() |
| 137 | + } |
| 138 | + |
| 139 | + fn diff_from_actual_frequency_smaller_than_threshold( |
| 140 | + result_frequency: f64, |
| 141 | + actual_frequency: f64, |
| 142 | + threshold: f64, |
| 143 | + ) -> bool { |
| 144 | + let result_diff_from_actual_freq = (result_frequency - actual_frequency).abs(); |
| 145 | + result_diff_from_actual_freq < threshold |
| 146 | + } |
| 147 | + |
| 148 | + fn interpolation_better_than_raw_result(result: YinResult, frequency: f64) -> bool { |
| 149 | + let result_frequency = result.get_frequency(); |
| 150 | + let refined_frequency = result.get_frequency_with_interpolation(); |
| 151 | + let result_diff = (result_frequency - frequency).abs(); |
| 152 | + let refined_diff = (refined_frequency - frequency).abs(); |
| 153 | + refined_diff < result_diff |
| 154 | + } |
| 155 | + |
| 156 | + #[test] |
| 157 | + fn test_simple_sine() { |
| 158 | + let sample_rate = 1000.0; |
| 159 | + let frequency = 12.0; |
| 160 | + let seconds = 10.0; |
| 161 | + let signal = generate_sine_wave(frequency, sample_rate, seconds); |
| 162 | + |
| 163 | + let min_expected_frequency = 10.0; |
| 164 | + let max_expected_frequency = 100.0; |
| 165 | + |
| 166 | + let yin = Yin::init( |
| 167 | + 0.1, |
| 168 | + min_expected_frequency, |
| 169 | + max_expected_frequency, |
| 170 | + sample_rate, |
| 171 | + ); |
| 172 | + |
| 173 | + let result = yin.yin(signal.as_slice()); |
| 174 | + assert!(result.is_ok()); |
| 175 | + let yin_result = result.unwrap(); |
| 176 | + |
| 177 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 178 | + yin_result.get_frequency(), |
| 179 | + frequency, |
| 180 | + 1.0 |
| 181 | + )); |
| 182 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 183 | + yin_result.get_frequency_with_interpolation(), |
| 184 | + frequency, |
| 185 | + 1.0, |
| 186 | + )); |
| 187 | + |
| 188 | + assert!(interpolation_better_than_raw_result(yin_result, frequency)); |
| 189 | + } |
| 190 | + |
| 191 | + #[test] |
| 192 | + fn test_sine_frequency_range() { |
| 193 | + let sample_rate = 5000.0; |
| 194 | + for freq in 30..50 { |
| 195 | + let frequency = freq as f64; |
| 196 | + let seconds = 2.0; |
| 197 | + let signal = generate_sine_wave(frequency, sample_rate, seconds); |
| 198 | + |
| 199 | + let min_expected_frequency = 5.0; |
| 200 | + let max_expected_frequency = 100.0; |
| 201 | + |
| 202 | + let yin = Yin::init( |
| 203 | + 0.1, |
| 204 | + min_expected_frequency, |
| 205 | + max_expected_frequency, |
| 206 | + sample_rate, |
| 207 | + ); |
| 208 | + let result = yin.yin(signal.as_slice()); |
| 209 | + assert!(result.is_ok()); |
| 210 | + let yin_result = result.unwrap(); |
| 211 | + |
| 212 | + if (sample_rate as i32 % freq) == 0 { |
| 213 | + assert_eq!(yin_result.get_frequency(), frequency); |
| 214 | + } else { |
| 215 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 216 | + yin_result.get_frequency(), |
| 217 | + frequency, |
| 218 | + 1.0 |
| 219 | + )); |
| 220 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 221 | + yin_result.get_frequency_with_interpolation(), |
| 222 | + frequency, |
| 223 | + 1.0, |
| 224 | + )); |
| 225 | + |
| 226 | + assert!(interpolation_better_than_raw_result(yin_result, frequency)); |
| 227 | + } |
| 228 | + } |
| 229 | + } |
| 230 | + |
| 231 | + #[test] |
| 232 | + fn test_harmonic_sines() { |
| 233 | + let sample_rate = 44100.0; |
| 234 | + let seconds = 2.0; |
| 235 | + let frequency_1 = 50.0; // Minimal/Fundamental frequency - this is what YIN should find |
| 236 | + let signal_1 = generate_sine_wave(frequency_1, sample_rate, seconds); |
| 237 | + let frequency_2 = 150.0; |
| 238 | + let signal_2 = generate_sine_wave(frequency_2, sample_rate, seconds); |
| 239 | + let frequency_3 = 300.0; |
| 240 | + let signal_3 = generate_sine_wave(frequency_3, sample_rate, seconds); |
| 241 | + |
| 242 | + let min_expected_frequency = 10.0; |
| 243 | + let max_expected_frequency = 500.0; |
| 244 | + |
| 245 | + let yin = Yin::init( |
| 246 | + 0.1, |
| 247 | + min_expected_frequency, |
| 248 | + max_expected_frequency, |
| 249 | + sample_rate, |
| 250 | + ); |
| 251 | + |
| 252 | + let total_samples = (sample_rate * seconds).round() as usize; |
| 253 | + let combined_signal: Vec<f64> = (0..total_samples) |
| 254 | + .map(|n| signal_1[n] + signal_2[n] + signal_3[n]) |
| 255 | + .collect(); |
| 256 | + |
| 257 | + let result = yin.yin(&combined_signal); |
| 258 | + assert!(result.is_ok()); |
| 259 | + let yin_result = result.unwrap(); |
| 260 | + |
| 261 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 262 | + yin_result.get_frequency(), |
| 263 | + frequency_1, |
| 264 | + 1.0 |
| 265 | + )); |
| 266 | + } |
| 267 | + |
| 268 | + #[test] |
| 269 | + fn test_unharmonic_sines() { |
| 270 | + let sample_rate = 44100.0; |
| 271 | + let seconds = 2.0; |
| 272 | + let frequency_1 = 50.0; |
| 273 | + let signal_1 = generate_sine_wave(frequency_1, sample_rate, seconds); |
| 274 | + let frequency_2 = 66.0; |
| 275 | + let signal_2 = generate_sine_wave(frequency_2, sample_rate, seconds); |
| 276 | + let frequency_3 = 300.0; |
| 277 | + let signal_3 = generate_sine_wave(frequency_3, sample_rate, seconds); |
| 278 | + |
| 279 | + let min_expected_frequency = 10.0; |
| 280 | + let max_expected_frequency = 500.0; |
| 281 | + |
| 282 | + let yin = Yin::init( |
| 283 | + 0.1, |
| 284 | + min_expected_frequency, |
| 285 | + max_expected_frequency, |
| 286 | + sample_rate, |
| 287 | + ); |
| 288 | + |
| 289 | + let total_samples = (sample_rate * seconds).round() as usize; |
| 290 | + let combined_signal: Vec<f64> = (0..total_samples) |
| 291 | + .map(|n| signal_1[n] + signal_2[n] + signal_3[n]) |
| 292 | + .collect(); |
| 293 | + |
| 294 | + let result = yin.yin(&combined_signal); |
| 295 | + assert!(result.is_ok()); |
| 296 | + let yin_result = result.unwrap(); |
| 297 | + |
| 298 | + let expected_frequency = (frequency_1 - frequency_2).abs(); |
| 299 | + assert!(diff_from_actual_frequency_smaller_than_threshold( |
| 300 | + yin_result.get_frequency(), |
| 301 | + expected_frequency, |
| 302 | + 1.0 |
| 303 | + )); |
| 304 | + assert!(interpolation_better_than_raw_result( |
| 305 | + yin_result, |
| 306 | + expected_frequency |
| 307 | + )); |
| 308 | + } |
| 309 | + |
| 310 | + #[test] |
| 311 | + fn test_err() { |
| 312 | + let sample_rate = 2500.0; |
| 313 | + let seconds = 2.0; |
| 314 | + let frequency = 440.0; |
| 315 | + |
| 316 | + // Can't find frequency 440 between 500 and 700 |
| 317 | + let min_expected_frequency = 500.0; |
| 318 | + let max_expected_frequency = 700.0; |
| 319 | + let yin = Yin::init( |
| 320 | + 0.1, |
| 321 | + min_expected_frequency, |
| 322 | + max_expected_frequency, |
| 323 | + sample_rate, |
| 324 | + ); |
| 325 | + |
| 326 | + let signal = generate_sine_wave(frequency, sample_rate, seconds); |
| 327 | + let result = yin.yin(&signal); |
| 328 | + assert!(result.is_err()); |
| 329 | + |
| 330 | + let yin_with_suitable_frequency_range = Yin::init( |
| 331 | + 0.1, |
| 332 | + min_expected_frequency - 100.0, |
| 333 | + max_expected_frequency, |
| 334 | + sample_rate, |
| 335 | + ); |
| 336 | + let result = yin_with_suitable_frequency_range.yin(&signal); |
| 337 | + assert!(result.is_ok()); |
| 338 | + } |
| 339 | +} |
0 commit comments