1use super::bands::{self, Bands, Smoothing};
4use super::onsets::spectral_flux;
5use super::sliding::{self, Stats, Unread, stats};
6use crate::fft::{fft, ifft};
7use crate::stft::hann_periodic;
8
9pub const SPLIT_HZ: f64 = 150.0;
11pub const MIN_PERIOD_SECS: f64 = 0.1;
13pub const MAX_PERIOD_SECS: f64 = 2.0;
15pub const PEAKS: usize = 3;
16pub const SMOOTHING: Smoothing = Smoothing {
20 hz: Some(10.0),
21 poles: 2,
22};
23
24pub const MODEL: &str = "onset strength: the spectral flux `onsets` takes, sum over a group's \
25 bands of max(0, e[n] - e[n-1]), run over the ERB bands of `bands` \
26 (Glasberg and Moore's filter widths), e each band's RMS envelope at \
27 `envelope_hz`, its energy smoothed before decimation by \
28 `smoothing.poles` one-pole lowpasses at `smoothing.hz` in place of the \
29 one at the band's own width `bands` reads through, so an onset is \
30 spread over some 30 ms; linear amplitude so a loud band outweighs a quiet one, \
31 from the filters' silent start; every channel's band energy \
32 averaged. A band is \
33 `low` where its centre is under `split_hz` and `rest` otherwise; \
34 `broadband` sums every band. The filters' skirts overlap, so a kick \
35 leaks into `rest` and a hat's attack into `low`, attenuated. That \
36 filterbank, smoothed envelope and rectified difference is Scheirer, E. D. \
37 (1998), Tempo and beat analysis of acoustic musical signals, J. Acoust. \
38 Soc. Am. 103(1) 588-601. Each window is `window_secs` of that envelope \
39 centred on t, t stepping by `hop_secs` from the target's start as \
40 every framed reading steps, clipped to the target at its ends, \
41 `from`/`to` its span; its mean is subtracted, y. height(tau) = sum over \
42 n < W - tau of y[n] y[n + tau] over sum of y[n]^2, the autocorrelation \
43 of the onset envelope Ellis, D. P. W. (2007), Beat tracking by dynamic \
44 programming, J. New Music Res. 36(1) 51-60, reads tempo from, \
45 normalized at lag 0 and biased: |height| <= 1 by Cauchy-Schwarz, \
46 taken by FFT and exact up to its rounding, and an envelope repeating \
47 exactly every tau peaks near 1 - tau/W, so of two periods that repeat \
48 equally the shorter reads higher, and heights at different periods or \
49 spans are not on one scale. place(tau): the same y under a periodic \
50 Hann taper w, sum of y[n] w[n] y[n + tau] w[n + tau] over sum of w[n] \
51 w[n + tau], the taper's own autocorrelation divided out: the window's \
52 ends no longer cut an onset abruptly, which in the plain sum skews a \
53 peak by up to a quarter lag, and the bias toward short lags is gone \
54 (Harris, F. J. (1978), On the use of windows for harmonic analysis \
55 with the discrete Fourier transform, Proc. IEEE 66(1) 51-83). peaks: \
56 lags L from `min_period_secs` to `max_period_secs` whose height is \
57 positive and whose place is over the lag before's and no lower than \
58 the lag after's, the highest three by height, highest first, each \
59 with the plain curve's height at L. period: L + d lags of \
60 `lag_step_secs`, d the vertex of the parabola through place at L - 1, \
61 L and L + 1 (Smith, J. O. and Serra, X. (1987), PARSHL, Proc. ICMC), \
62 |d| <= 1/2; bpm = 60 / period. Bounds: under the model that place is \
63 symmetric about the true period P and falls strictly away from it, L \
64 reading at least its neighbours puts P within half a lag of L, and \
65 the higher neighbour names the half: P lies in [L, L + 1/2] where \
66 place at L + 1 is over place at L - 1, in [L - 1/2, L] otherwise; d \
67 lies in that half too, so P is within r of the period, r the farther \
68 end of that half from it, r <= 1/2 lag. `period_bounds_secs` is \
69 period -+ r and `bpm_bounds` 60 over those. They cover the lag grid \
70 and the interpolation alone, under that model, which no window can \
71 confirm: an envelope not periodic across the window (a tempo moving, \
72 onsets jittered) is outside it, and its place need not peak at any \
73 one period. Both are null where the window refutes the model, place \
74 not falling strictly from L - 1 to L - 2 and from L + 1 to L + 2, or \
75 leaves the stated span, clipped at the target's ends. No tempo prior \
76 (Ellis weights by a log-Gaussian) and no octave choice: a beat, its \
77 half and its double may each lead, and each is reported; no inter-onset \
78 clustering or beat tracking (Dixon, S. (2001), Automatic extraction \
79 of tempo and beat from expressive performances, J. New Music Res. \
80 30(1) 39-58) is done. drift_bpm: the leading peak's bpm less the \
81 previous window's, null where either has none, so a lead jumping \
82 between a beat and its double reads as a large drift. curve, where \
83 asked: the height at every lag from `min_period_secs` by \
84 `lag_step_secs` to the window's longest; heights and curves are null \
85 where the window's envelope is constant. summary: over the windows \
86 with a peak, the leading peak's bpm and height and the drift, their \
87 mean, min and max with the t each stands at. Exact path: none; a \
88 closed form, its onsets placed exactly or not, is read off its \
89 rendered samples like any other, because the onset strength is the \
90 band envelopes' rise, which no closed form states";
91
92#[derive(Clone, Copy, Debug, PartialEq, Eq)]
93pub enum Group {
94 Broadband,
95 Low,
96 Rest,
97}
98
99impl Group {
100 pub const ALL: [Group; 3] = [Group::Broadband, Group::Low, Group::Rest];
101
102 pub fn key(self) -> &'static str {
103 match self {
104 Group::Broadband => "broadband",
105 Group::Low => "low",
106 Group::Rest => "rest",
107 }
108 }
109}
110
111#[derive(Clone, Copy, Debug, PartialEq)]
112pub struct Peak {
113 pub period_secs: f64,
114 pub period_bounds_secs: Option<(f64, f64)>,
117 pub bpm: f64,
118 pub bpm_bounds: Option<(f64, f64)>,
120 pub height: f64,
122}
123
124#[derive(Clone, Debug, PartialEq)]
125pub struct Window {
126 pub t_secs: f64,
127 pub from_secs: f64,
128 pub to_secs: f64,
129 pub peaks: Vec<Peak>,
131 pub drift_bpm: Option<f64>,
132 pub curve: Option<Vec<f64>>,
134}
135
136#[derive(Clone, Copy, Debug, PartialEq)]
137pub struct Summary {
138 pub windows_with_peak: usize,
139 pub bpm: Option<Stats>,
140 pub height: Option<Stats>,
141 pub drift_bpm: Option<Stats>,
142}
143
144#[derive(Clone, Debug, PartialEq)]
145pub struct Track {
146 pub group: Group,
147 pub centres_hz: Vec<f64>,
148 pub windows: Vec<Window>,
149 pub summary: Summary,
150}
151
152#[derive(Clone, Debug, PartialEq)]
153pub struct Periodicity {
154 pub hop_secs: f64,
155 pub window_secs: f64,
156 pub envelope_hz: f64,
157 pub lag_step_secs: f64,
158 pub min_period_secs: f64,
159 pub max_period_secs: f64,
160 pub tracks: Vec<Track>,
161}
162
163pub fn measure(
164 planes: &[&[f64]],
165 sample_rate: f64,
166 start_secs: f64,
167 hop_secs: f64,
168 window_secs: f64,
169 curve: bool,
170) -> Result<Periodicity, Unread> {
171 let held = crate::measure::held(planes);
172 sliding::fits(held, sample_rate, window_secs, MAX_PERIOD_SECS)?;
173 let split = bands::smoothed(planes, sample_rate, start_secs, SMOOTHING);
174 let rate = split.rate_hz;
175 let lags = Lags {
176 min: (MIN_PERIOD_SECS * rate).ceil() as usize,
177 max: (MAX_PERIOD_SECS * rate).floor() as usize,
178 rate,
179 };
180 let len = split.bands.first().map_or(0, |b| b.rms.len());
181 let grid = sliding::spans(
182 held,
183 sample_rate,
184 start_secs,
185 hop_secs,
186 rate,
187 window_secs,
188 len,
189 );
190 let size = (grid.window_secs * rate).round() as usize;
191 let tracks = Group::ALL
192 .into_iter()
193 .zip(envelopes(&split))
194 .map(|(group, (centres_hz, envelope))| {
195 let mut windows: Vec<Window> = grid
196 .spans
197 .iter()
198 .map(|s| {
199 let (heights, place) = heights(&envelope[s.from..s.to], lags.max + 1);
200 Window {
201 t_secs: s.t_secs,
202 from_secs: s.from_secs,
203 to_secs: s.to_secs,
204 peaks: lags.peaks(&heights, &place, s.to - s.from == size),
205 drift_bpm: None,
206 curve: curve.then(|| {
207 heights[..heights.len().min(lags.max + 1)]
208 .iter()
209 .skip(lags.min)
210 .copied()
211 .collect()
212 }),
213 }
214 })
215 .collect();
216 for i in 1..windows.len() {
217 let lead = |w: &Window| w.peaks.first().map(|p| p.bpm);
218 windows[i].drift_bpm = lead(&windows[i])
219 .zip(lead(&windows[i - 1]))
220 .map(|(a, b)| a - b);
221 }
222 Track {
223 group,
224 centres_hz,
225 summary: summary(&windows),
226 windows,
227 }
228 })
229 .collect();
230 Ok(Periodicity {
231 hop_secs: grid.hop_secs,
232 window_secs: grid.window_secs,
233 envelope_hz: rate,
234 lag_step_secs: 1.0 / rate,
235 min_period_secs: lags.min as f64 / rate,
236 max_period_secs: lags.max as f64 / rate,
237 tracks,
238 })
239}
240
241fn envelopes(split: &Bands) -> Vec<(Vec<f64>, Vec<f64>)> {
243 let tracks = &split.bands;
244 let low = tracks
245 .iter()
246 .position(|b| b.centre_hz >= SPLIT_HZ)
247 .unwrap_or(tracks.len());
248 let ranges = [0..tracks.len(), 0..low, low..tracks.len()];
249 let len = tracks.first().map_or(0, |b| b.rms.len());
250 let mut flux: Vec<Vec<f64>> = ranges.iter().map(|_| vec![0.0; len]).collect();
251 let (mut prev, mut curr) = (vec![0.0; tracks.len()], vec![0.0; tracks.len()]);
252 for n in 0..len {
253 for (held, band) in curr.iter_mut().zip(tracks) {
254 *held = band.rms[n];
255 }
256 if n > 0 {
257 for (out, range) in flux.iter_mut().zip(&ranges) {
258 out[n] = spectral_flux(&prev[range.clone()], &curr[range.clone()]);
259 }
260 }
261 std::mem::swap(&mut prev, &mut curr);
262 }
263 ranges
264 .into_iter()
265 .zip(flux)
266 .map(|(range, envelope)| {
267 let centres = tracks[range].iter().map(|b| b.centre_hz).collect();
268 (centres, envelope)
269 })
270 .collect()
271}
272
273fn heights(segment: &[f64], top: usize) -> (Vec<f64>, Vec<f64>) {
276 let top = top.min(segment.len().saturating_sub(1));
277 let mean = segment.iter().sum::<f64>() / segment.len() as f64;
278 let y: Vec<f64> = segment.iter().map(|x| x - mean).collect();
279 let taper = hann_periodic(segment.len());
280 let tapered: Vec<f64> = y.iter().zip(&taper).map(|(y, w)| y * w).collect();
281 let plain = correlated(&y, top);
282 let (curve, own) = (correlated(&tapered, top), correlated(&taper, top));
283 (
284 plain.iter().map(|r| r / plain[0]).collect(),
285 curve.iter().zip(&own).map(|(c, w)| c / w).collect(),
286 )
287}
288
289fn correlated(x: &[f64], top: usize) -> Vec<f64> {
291 let n = (x.len() + top + 1).next_power_of_two();
292 let mut re = vec![0.0; n];
293 re[..x.len()].copy_from_slice(x);
294 let mut im = vec![0.0; n];
295 fft(&mut re, &mut im);
296 for (r, i) in re.iter_mut().zip(im.iter_mut()) {
297 *r = *r * *r + *i * *i;
298 *i = 0.0;
299 }
300 ifft(&mut re, &mut im);
301 re.truncate(top + 1);
302 re
303}
304
305struct Lags {
306 min: usize,
307 max: usize,
308 rate: f64,
309}
310
311impl Lags {
312 fn peaks(&self, heights: &[f64], place: &[f64], whole: bool) -> Vec<Peak> {
315 let last = self.max.min(heights.len().saturating_sub(2));
316 let mut found: Vec<(usize, f64)> = (self.min.max(1)..=last)
317 .filter(|&l| heights[l] > 0.0 && place[l] > place[l - 1] && place[l] >= place[l + 1])
318 .map(|l| (l, heights[l]))
319 .collect();
320 found.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
321 found
322 .into_iter()
323 .take(PEAKS)
324 .map(|(lag, height)| {
325 let (before, at, after) = (place[lag - 1], place[lag], place[lag + 1]);
326 let shift = 0.5 * (before - after) / (before - 2.0 * at + after);
327 let half = match after > before {
328 true => (0.0, 0.5),
329 false => (-0.5, 0.0),
330 };
331 let reach = (shift - half.0).max(half.1 - shift);
332 let secs = |l: f64| (lag as f64 + l) / self.rate;
333 let period_secs = secs(shift);
334 let falls = place[lag - 2] < before
335 && place.get(lag + 2).is_some_and(|&beyond| beyond < after);
336 let bounds = (whole && falls).then(|| (secs(shift - reach), secs(shift + reach)));
337 Peak {
338 period_secs,
339 period_bounds_secs: bounds,
340 bpm: 60.0 / period_secs,
341 bpm_bounds: bounds.map(|(lo, hi)| (60.0 / hi, 60.0 / lo)),
342 height,
343 }
344 })
345 .collect()
346 }
347}
348
349fn summary(windows: &[Window]) -> Summary {
350 let leads = || {
351 windows
352 .iter()
353 .filter_map(|w| w.peaks.first().map(|p| (w.t_secs, *p)))
354 };
355 Summary {
356 windows_with_peak: leads().count(),
357 bpm: stats(leads().map(|(t, p)| (t, p.bpm))),
358 height: stats(leads().map(|(t, p)| (t, p.height))),
359 drift_bpm: stats(
360 windows
361 .iter()
362 .filter_map(|w| w.drift_bpm.map(|d| (w.t_secs, d))),
363 ),
364 }
365}