Classify takes with numbl algorithms by default (silence_gap_knn from audio-spike-sorting, noise-floor adapted), with a custom-algorithm editor and the feature kNN kept as an option
17 changed files+2478−66
README.mdmodified+23−7View file
@@ -33,13 +33,29 @@ rate, attack and decay times. Loudness is deliberately kept out of this
3333 vector; it is expression, not identity, so playing softly does not change
3434 which voice a hit lands on.
3535
36-A take goes through the same detector, and each hit is classified by
37-distance-weighted nearest neighbours over the pooled training set,
38-z-scored per dimension. The timeline shows one lane per voice with a
39-marker per hit, bar height showing strength. The classifier will sometimes
40-be wrong, and the fix is one gesture: drag the marker to the right lane.
41-"Learn" folds the corrected hit back into that voice's training set, so
42-the labelling improves with use; there is no model to refit.
36+A take is detected and classified by a MATLAB-syntax algorithm executed
37+in the browser by [numbl](https://numbl.org), with the same interface the
38+[audio-spike-sorting](../audio-spike-sorting) benchmark uses
39+(`[times, labels] = name(x, fs, training)`, everything at 16 kHz), so an
40+algorithm developed and scored against that benchmark drops in here
41+unchanged. The default is silence_gap_knn from the benchmark, with one
42+adaptation: its segmentation threshold is raised from a fraction of the
43+recording peak to a robust noise-floor estimate, since a microphone's
44+background is never digitally silent. A classifier selector in the take
45+bar switches between the default (source editable, resettable), a custom
46+numbl algorithm pasted into the same editor, and the original in-app
47+method: spectral-flux onset detection plus distance-weighted nearest
48+neighbours over the pooled per-hit feature vectors. The training the
49+numbl algorithms see is the raw snippets cut from the voices' clips
50+(20 ms before each kept onset to 180 ms after); hits learned from takes
51+carry only features, so they inform the built-in kNN but not the numbl
52+path.
53+
54+The timeline shows one lane per voice with a marker per hit, bar height
55+showing strength. The classifier will sometimes be wrong, and the fix is
56+one gesture: drag the marker to the right lane. "Learn" folds the
57+corrected hit back into that voice's feature training set, so the
58+built-in kNN improves with use; there is no model to refit.
4359
4460 Everything persists in the browser: voices and their training, the take
4561 (audio and edited events), per-instrument geometry, slider positions, and
index.htmlmodified+25−1View file
@@ -43,6 +43,11 @@
4343 .voice-row progress { width: 6rem; }
4444 .voice-row input.vol { width: 5rem; }
4545 #iwedit { border: 1px solid rgba(128,128,128,0.35); border-radius: 8px; padding: 0.6rem 0.8rem; margin: 0.5rem 0; }
46+ #clf-editor { border: 1px solid rgba(128,128,128,0.35); border-radius: 8px; padding: 0.6rem 0.8rem; margin: 0.5rem 0; }
47+ #clf-editor textarea {
48+ width: 100%; min-height: 20rem; font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
49+ font-size: 0.8rem; line-height: 1.4; tab-size: 4; box-sizing: border-box;
50+ }
4651 .iwedit-grid { display: grid; grid-template-columns: minmax(0, 1fr) 24rem; gap: 0 1.5rem; align-items: start; }
4752 @media (max-width: 56rem) { .iwedit-grid { grid-template-columns: 1fr; } }
4853 .params { display: grid; grid-template-columns: repeat(auto-fill, minmax(14rem, 1fr)); gap: 0.3rem 1.5rem; }
@@ -102,12 +107,31 @@
102107 <div class="bar">
103108 <button id="take-record">● Record take</button>
104109 <div class="meter" id="meter" hidden><div></div></div>
105- <label>sensitivity <input id="take-sens" type="range" min="0" max="1" step="0.01" value="0.6" /></label>
110+ <label>classifier
111+ <select id="clf-method">
112+ <option value="builtin">silence_gap_knn (numbl)</option>
113+ <option value="custom">custom (numbl)</option>
114+ <option value="legacy">feature kNN (built-in)</option>
115+ </select>
116+ </label>
117+ <button id="clf-edit">algorithm…</button>
118+ <span id="take-sens-wrap"><label>sensitivity <input id="take-sens" type="range" min="0" max="1" step="0.01" value="0.6" /></label></span>
106119 <button id="take-redetect" disabled>Re-detect & classify</button>
107120 <span style="flex:1"></span>
108121 <button id="take-play" disabled>Play</button>
109122 <button id="take-save" disabled>Save .wav</button>
110123 </div>
124+ <div id="clf-editor" hidden>
125+ <div class="bar">
126+ <strong id="clf-title"></strong>
127+ <span class="hint">function [times, labels] = name(x, fs, training) · the audio-spike-sorting interface, so an algorithm developed there drops in unchanged</span>
128+ <span style="flex:1"></span>
129+ <button id="clf-reset" hidden>Reset to default</button>
130+ <button id="clf-close">Close</button>
131+ </div>
132+ <textarea id="clf-source" spellcheck="false"></textarea>
133+ </div>
134+
111135 <div id="iwedit" hidden>
112136 <div class="bar">
113137 <strong id="iwedit-title"></strong>
package-lock.jsonmodified+1807−48View file
@@ -9,7 +9,8 @@
99 "version": "0.1.0",
1010 "license": "Apache-2.0",
1111 "dependencies": {
12- "instrawave": "file:../instrawave"
12+ "instrawave": "file:../instrawave",
13+ "numbl": "^0.4.19"
1314 },
1415 "devDependencies": {
1516 "@types/node": "^26.1.1",
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17093275 }
17103276 },
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3281+ "license": "MIT",
3282+ "funding": {
3283+ "type": "github",
3284+ "url": "https://github.com/sponsors/wooorm"
3285+ }
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3287+ "node_modules/stringify-entities": {
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3291+ "license": "MIT",
3292+ "dependencies": {
3293+ "character-entities-html4": "^2.0.0",
3294+ "character-entities-legacy": "^3.0.0"
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3305+ "license": "MIT",
3306+ "dependencies": {
3307+ "style-to-object": "1.0.14"
3308+ }
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3314+ "license": "MIT",
3315+ "dependencies": {
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3323+ "license": "MIT"
3324+ },
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17133327 "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.17.tgz",
@@ -1725,6 +3339,26 @@
17253339 "url": "https://github.com/sponsors/SuperchupuDev"
17263340 }
17273341 },
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3346+ "license": "MIT",
3347+ "funding": {
3348+ "type": "github",
3349+ "url": "https://github.com/sponsors/wooorm"
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3356+ "license": "MIT",
3357+ "funding": {
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3359+ "url": "https://github.com/sponsors/wooorm"
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@@ -1746,6 +3380,121 @@
17463380 "dev": true,
17473381 "license": "MIT"
17483382 },
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3390+ "bail": "^2.0.0",
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3402+ "node_modules/unist-util-is": {
3403+ "version": "6.0.1",
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3484+ "node_modules/vfile-message": {
3485+ "version": "4.0.3",
3486+ "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.3.tgz",
3487+ "integrity": "sha512-QTHzsGd1EhbZs4AsQ20JX1rC3cOlt/IWJruk893DfLRr57lcnOeMaWG4K0JrRta4mIJZKth2Au3mM3u03/JWKw==",
3488+ "license": "MIT",
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3490+ "@types/unist": "^3.0.0",
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17493498 "node_modules/vite": {
17503499 "version": "5.4.21",
17513500 "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz",
@@ -1906,6 +3655,16 @@
19063655 "optional": true
19073656 }
19083657 }
3658+ },
3659+ "node_modules/zwitch": {
3660+ "version": "2.0.4",
3661+ "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
3662+ "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==",
3663+ "license": "MIT",
3664+ "funding": {
3665+ "type": "github",
3666+ "url": "https://github.com/sponsors/wooorm"
3667+ }
19093668 }
19103669 }
19113670 }
package.jsonmodified+5−4View file
@@ -13,13 +13,14 @@
1313 "test": "vite-node scripts/test-node.ts"
1414 },
1515 "dependencies": {
16- "instrawave": "file:../instrawave"
16+ "instrawave": "file:../instrawave",
17+ "numbl": "^0.4.19"
1718 },
1819 "devDependencies": {
1920 "@types/node": "^26.1.1",
21+ "@webgpu/types": "^0.1.44",
2022 "typescript": "^5.5.0",
2123 "vite": "^5.4.0",
22- "vite-node": "^6.0.0",
23- "@webgpu/types": "^0.1.44"
24+ "vite-node": "^6.0.0"
2425 }
25-}
26+}
scripts/test-node.tsmodified+68−0View file
@@ -123,5 +123,73 @@ function burst(x: Float32Array, t0: number, freq: number, decay: number, amp: nu
123123 check(v.getInt16(50, true) === 32767, 'wav: over-range clips to full scale');
124124 }
125125
126+// --- numbl classifier path ---------------------------------------------------------
127+// The default silence_gap_knn (with its noise-floor threshold adaptation)
128+// run over the same pipeline the app uses: training snippets cut from
129+// voice clips at 16 kHz, the take reduced to 16 kHz, detections mapped
130+// back to voices. The take carries a realistic microphone noise floor,
131+// which the original benchmark algorithm's silence assumption would not
132+// survive; this is exactly what the adaptation is for.
133+
134+const { buildVoiceTraining } = await import('../src/numbl/snippets.ts');
135+const { runAlgorithm } = await import('../src/numbl/execute.ts');
136+const { downsampleBox } = await import('../src/numbl/dsp.ts');
137+const { DEFAULT_CLASSIFIER_SOURCE } = await import('../src/numbl/index.ts');
138+const { CLF_FS } = await import('../src/numbl/types.ts');
139+
140+{
141+ const noiseInto = (x: Float32Array, amp: number, seed: number) => {
142+ let s = seed;
143+ for (let i = 0; i < x.length; i++) {
144+ s = (s * 1103515245 + 12345) & 0x7fffffff;
145+ x[i] += (s / 0x3fffffff - 1) * amp;
146+ }
147+ };
148+
149+ // one training clip per voice: 8 well-separated bursts at known times
150+ const clipAudio = new Map<string, { samples: Float32Array; sr: number }>();
151+ const mkVoice = (id: string, freq: number, decay: number, seed: number) => {
152+ const times = Array.from({ length: 8 }, (_, i) => 0.3 + i * 0.35);
153+ const samples = new Float32Array(Math.floor(3.2 * SR));
154+ noiseInto(samples, 5e-4, seed);
155+ times.forEach((t, i) => burst(samples, t, freq, decay, 0.5, seed + i));
156+ clipAudio.set(`clip-${id}`, { samples, sr: SR });
157+ return {
158+ id, name: id, color: '#888',
159+ binding: { kind: 'synth', preset: 'kick' } as const,
160+ volume: 1,
161+ clips: [{ id: `clip-${id}`, sr: SR, sens: 0.6, onsets: times, excluded: [], hits: [] }],
162+ loose: [],
163+ };
164+ };
165+ const testVoices = [mkVoice('thump', 200, 0.05, 11), mkVoice('tick', 1500, 0.015, 77)];
166+
167+ const { training, voiceIds, skipped } = await buildVoiceTraining(
168+ testVoices,
169+ (id) => Promise.resolve(clipAudio.get(id) ?? null),
170+ );
171+ check(voiceIds.length === 2 && skipped.length === 0,
172+ `numbl training: 2 voices, ${training.snips.map((s) => s.count).join('+')} snippets`);
173+
174+ // a take with a mic-like noise floor and 6 alternating hits
175+ const takeTimes = [0.4, 0.8, 1.2, 1.6, 2.0, 2.4];
176+ const takeSamples = new Float32Array(Math.floor(3 * SR));
177+ noiseInto(takeSamples, 5e-4, 999);
178+ takeTimes.forEach((t, i) => burst(takeSamples, t, i % 2 === 0 ? 200 : 1500, i % 2 === 0 ? 0.05 : 0.015, 0.4, 300 + i));
179+
180+ const x16 = downsampleBox(takeSamples, SR, CLF_FS);
181+ const { detections } = runAlgorithm(DEFAULT_CLASSIFIER_SOURCE, x16, CLF_FS, training);
182+ check(detections.length === takeTimes.length,
183+ `numbl classify: ${detections.length} of ${takeTimes.length} hits found on a noisy take`);
184+ let timesOk = true, labelsOk = true;
185+ detections.forEach((d, i) => {
186+ if (i >= takeTimes.length) return;
187+ if (Math.abs(d.t - takeTimes[i]) > 0.03) timesOk = false;
188+ if (d.typeIndex !== i % 2) labelsOk = false;
189+ });
190+ check(timesOk, 'numbl classify: every hit within 30 ms of the truth');
191+ check(labelsOk, 'numbl classify: thump/tick labels all correct');
192+}
193+
126194 console.log(failures ? `\n${failures} failure(s)` : '\nall checks passed');
127195 process.exit(failures ? 1 : 0);
src/m-raw.d.tsadded+1−0View file
@@ -0,0 +1 @@
1+declare module "*.m?raw" { const src: string; export default src; }
src/main.tsmodified+102−5View file
@@ -20,11 +20,17 @@ import { IwEngine, parseBake, PhaseCancelled, type StimulusEvent } from './rende
2020 import { Viewer, type Camera } from 'instrawave/src/host/view.ts';
2121 import {
2222 clearBulk, clearStore, deleteClipAudio, getBakeBytes, getBakeKey, getClipAudio, getTakeAudio,
23- deleteRender, getRender, loadIwOverrides, loadTakeHits, loadUi, loadVoices, putBake, putClipAudio,
23+ deleteRender, getRender, loadClassifier, loadIwOverrides, loadTakeHits, loadUi, loadVoices,
24+ putBake, putClipAudio,
2425 putRender, putTakeAudio,
25- saveIwOverrides, saveTakeHits, saveUi, saveVoices, trainPool,
26+ saveClassifier, saveIwOverrides, saveTakeHits, saveUi, saveVoices, trainPool,
2627 type TakeHit, type TrainClip, type Voice,
2728 } from './store.ts';
29+import { CUSTOM_CLASSIFIER_TEMPLATE, DEFAULT_CLASSIFIER_NAME, DEFAULT_CLASSIFIER_SOURCE } from './numbl/index.ts';
30+import { buildVoiceTraining } from './numbl/snippets.ts';
31+import { classifyInWorker } from './numbl/client.ts';
32+import { downsampleBox } from './numbl/dsp.ts';
33+import { CLF_FS } from './numbl/types.ts';
2834 import { WaveView, type Marker } from './ui/waveform.ts';
2935 import { Timeline } from './ui/timeline.ts';
3036
@@ -676,10 +682,56 @@ async function startBake(name: string): Promise<void> {
676682
677683 const timeline = new Timeline($('timeline') as HTMLCanvasElement);
678684
679-function processTake(): void {
685+const clf = loadClassifier();
686+const activeClfSource = (): string =>
687+ clf.method === 'custom' ? (clf.customSrc ?? CUSTOM_CLASSIFIER_TEMPLATE)
688+ : (clf.builtinSrc ?? DEFAULT_CLASSIFIER_SOURCE);
689+
690+async function processTake(): Promise<void> {
680691 if (!take.clip) return;
681692 stopPlayback();
682693 const { samples, sr } = take.clip;
694+ if (clf.method === 'legacy') { processTakeLegacy(samples, sr); return; }
695+
696+ const btn = $('take-redetect') as HTMLButtonElement;
697+ btn.disabled = true;
698+ status('classifying take with numbl...');
699+ try {
700+ const { training, voiceIds, skipped } = await buildVoiceTraining(voices, getClipAudio);
701+ if (voiceIds.length === 0) {
702+ status('no voice has training clips with audio; record examples first, or switch the classifier to the built-in feature kNN');
703+ return;
704+ }
705+ const x16 = downsampleBox(samples, sr, CLF_FS);
706+ const { detections } = await classifyInWorker(activeClfSource(), x16, CLF_FS, training);
707+
708+ const hits: TakeHit[] = [];
709+ let refDb = -160;
710+ for (let i = 0; i < detections.length; i++) {
711+ const det = detections[i];
712+ if (det.typeIndex < 0 || det.typeIndex >= voiceIds.length) continue;
713+ const s0 = Math.max(0, Math.min(samples.length - 1, Math.floor(det.t * sr)));
714+ const s1 = i + 1 < detections.length ? Math.floor(detections[i + 1].t * sr) : samples.length;
715+ const f = extractHit(samples, sr, s0, Math.max(s1, s0 + 1));
716+ if (f.peakDb > refDb) refDb = f.peakDb;
717+ hits.push({ id: uid(), t: det.t, voiceId: voiceIds[det.typeIndex], gain: 0, peakDb: f.peakDb, vec: f.vec, s0, s1 });
718+ }
719+ for (const h of hits) h.gain = gainFromPeakDb(h.peakDb, refDb);
720+ take.hits = hits;
721+ saveTakeHits(take.hits);
722+ refreshTimeline();
723+ const algoName = clf.method === 'builtin' ? DEFAULT_CLASSIFIER_NAME : 'the custom algorithm';
724+ const skippedNote = skipped.length ? `; no training audio for ${skipped.join(', ')}` : '';
725+ status(`${hits.length} hits classified by ${algoName} across ${voiceIds.length} trained voice${voiceIds.length === 1 ? '' : 's'}${skippedNote}`);
726+ } catch (err) {
727+ status(`classifier failed: ${err instanceof Error ? err.message : err}; previous hits kept`);
728+ } finally {
729+ btn.disabled = take.clip === null;
730+ syncTakeBar();
731+ }
732+}
733+
734+function processTakeLegacy(samples: Float32Array, sr: number): void {
683735 const sens = Number(($('take-sens') as HTMLInputElement).value);
684736 const onsets = detectOnsets(samples, sr, sens);
685737 const trained = voices.filter((v) => trainPool(v).length > 0);
@@ -746,11 +798,55 @@ $('take-record').addEventListener('click', () => {
746798 else void startRecording({ kind: 'take' });
747799 });
748800
749-$('take-redetect').addEventListener('click', processTake);
801+$('take-redetect').addEventListener('click', () => void processTake());
750802 $('take-sens').addEventListener('change', () => {
751803 ui.takeSens = Number(($('take-sens') as HTMLInputElement).value);
752804 saveUi(ui);
753- if (take.clip) processTake();
805+ if (take.clip) void processTake();
806+});
807+
808+// ---- classifier selection and editor ---------------------------------------
809+
810+const clfMethodSel = $('clf-method') as HTMLSelectElement;
811+
812+function syncClfBar(): void {
813+ clfMethodSel.value = clf.method;
814+ ($('take-sens-wrap') as HTMLElement).hidden = clf.method !== 'legacy';
815+ ($('clf-edit') as HTMLButtonElement).disabled = clf.method === 'legacy';
816+}
817+
818+function syncClfEditor(): void {
819+ if ($('clf-editor').hidden) return;
820+ $('clf-title').textContent = clf.method === 'custom' ? 'custom classifier' : DEFAULT_CLASSIFIER_NAME;
821+ ($('clf-source') as HTMLTextAreaElement).value = activeClfSource();
822+ ($('clf-reset') as HTMLButtonElement).hidden = clf.method !== 'builtin' || clf.builtinSrc === null;
823+}
824+
825+clfMethodSel.addEventListener('change', () => {
826+ clf.method = clfMethodSel.value as typeof clf.method;
827+ saveClassifier(clf);
828+ syncClfBar();
829+ if (clf.method === 'legacy') $('clf-editor').hidden = true;
830+ else syncClfEditor();
831+ if (take.clip) void processTake();
832+});
833+
834+$('clf-edit').addEventListener('click', () => {
835+ $('clf-editor').hidden = false;
836+ syncClfEditor();
837+});
838+$('clf-close').addEventListener('click', () => { $('clf-editor').hidden = true; });
839+($('clf-source') as HTMLTextAreaElement).addEventListener('change', () => {
840+ const src = ($('clf-source') as HTMLTextAreaElement).value;
841+ if (clf.method === 'custom') clf.customSrc = src;
842+ else clf.builtinSrc = src === DEFAULT_CLASSIFIER_SOURCE ? null : src;
843+ saveClassifier(clf);
844+ syncClfEditor();
845+});
846+$('clf-reset').addEventListener('click', () => {
847+ clf.builtinSrc = null;
848+ saveClassifier(clf);
849+ syncClfEditor();
754850 });
755851
756852 timeline.onSelect = (id) => {
@@ -1113,6 +1209,7 @@ $('iwedit-done').addEventListener('click', closeIwEditor);
11131209
11141210 ($('capture-sens') as HTMLInputElement).value = String(ui.captureSens);
11151211 ($('take-sens') as HTMLInputElement).value = String(ui.takeSens);
1212+syncClfBar();
11161213 take.hits = loadTakeHits() ?? [];
11171214 void getTakeAudio().then((a) => {
11181215 if (a) {
src/numbl/algo.worker.tsadded+36−0View file
@@ -0,0 +1,36 @@
1+/// <reference lib="webworker" />
2+/** Runs the classifier off the main thread so a slow or broken custom
3+ * algorithm cannot freeze the page. One request, one response. */
4+import { runAlgorithm } from './execute.ts';
5+import type { Detection, TrainingArrays } from './types.ts';
6+
7+export interface ClfRequest {
8+ id: number;
9+ source: string;
10+ x: Float32Array;
11+ fs: number;
12+ training: TrainingArrays;
13+}
14+
15+export interface ClfResponse {
16+ id: number;
17+ ok: boolean;
18+ detections?: Detection[];
19+ logs: string[];
20+ error?: string;
21+}
22+
23+self.onmessage = (e: MessageEvent<ClfRequest>) => {
24+ const { id, source, x, fs, training } = e.data;
25+ try {
26+ const out = runAlgorithm(source, x, fs, training);
27+ const resp: ClfResponse = { id, ok: true, ...out };
28+ self.postMessage(resp);
29+ } catch (err) {
30+ const resp: ClfResponse = {
31+ id, ok: false, logs: [],
32+ error: err instanceof Error ? err.message : String(err),
33+ };
34+ self.postMessage(resp);
35+ }
36+};
src/numbl/client.tsadded+36−0View file
@@ -0,0 +1,36 @@
1+/** Main-thread side of the classifier worker: one call per take, a fresh
2+ * worker each time so a hung custom algorithm dies with its worker when
3+ * the next classification starts. */
4+import type { ClfRequest, ClfResponse } from './algo.worker.ts';
5+import type { Detection, TrainingArrays } from './types.ts';
6+
7+let current: Worker | null = null;
8+let nextId = 1;
9+
10+export function classifyInWorker(
11+ source: string,
12+ x: Float32Array,
13+ fs: number,
14+ training: TrainingArrays,
15+): Promise<{ detections: Detection[]; logs: string[] }> {
16+ current?.terminate();
17+ const worker = new Worker(new URL('./algo.worker.ts', import.meta.url), { type: 'module' });
18+ current = worker;
19+ const id = nextId++;
20+ const req: ClfRequest = { id, source, x, fs, training };
21+ return new Promise((resolve, reject) => {
22+ worker.onmessage = (e: MessageEvent<ClfResponse>) => {
23+ if (e.data.id !== id) return;
24+ worker.terminate();
25+ if (current === worker) current = null;
26+ if (e.data.ok && e.data.detections) resolve({ detections: e.data.detections, logs: e.data.logs });
27+ else reject(new Error(e.data.error ?? 'classifier failed'));
28+ };
29+ worker.onerror = (e) => {
30+ worker.terminate();
31+ if (current === worker) current = null;
32+ reject(new Error(e.message || 'classifier worker failed'));
33+ };
34+ worker.postMessage(req);
35+ });
36+}
src/numbl/dsp.tsadded+52−0View file
@@ -0,0 +1,52 @@
1+/**
2+ * Rate reduction and onset refinement for the classifier path, both
3+ * carried over from audio-spike-sorting so snippets cut here match the
4+ * ones the benchmark cuts.
5+ */
6+
7+/** Downsample by box-averaging the input samples covered by each output
8+ * sample; a crude but adequate anti-alias for percussive material. */
9+export function downsampleBox(x: Float32Array, srFrom: number, srTo: number): Float32Array {
10+ if (srFrom === srTo) return x;
11+ const n = Math.floor((x.length * srTo) / srFrom);
12+ const out = new Float32Array(n);
13+ for (let i = 0; i < n; i++) {
14+ const a = Math.floor((i * srFrom) / srTo);
15+ const b = Math.max(a + 1, Math.floor(((i + 1) * srFrom) / srTo));
16+ let s = 0;
17+ for (let j = a; j < b && j < x.length; j++) s += x[j];
18+ out[i] = s / (b - a);
19+ }
20+ return out;
21+}
22+
23+/** Refine a coarse onset to the foot of the energy rise: find the
24+ * short-term energy peak within a window after the coarse time, then
25+ * walk back to where the envelope first falls below a small fraction of
26+ * that peak. */
27+export function refineOnset(x: Float32Array, sr: number, tCoarse: number): number {
28+ const w = Math.max(1, Math.round(0.002 * sr));
29+ const from = Math.max(0, Math.round((tCoarse - 0.03) * sr));
30+ const to = Math.min(x.length - w, Math.round((tCoarse + 0.05) * sr));
31+ if (to <= from) return Math.round(tCoarse * sr);
32+
33+ const env = (s: number) => {
34+ let e = 0;
35+ for (let i = 0; i < w; i++) e += x[s + i] * x[s + i];
36+ return e;
37+ };
38+ let peakS = from, peakE = 0;
39+ for (let s = from; s <= to; s += w) {
40+ const e = env(s);
41+ if (e > peakE) { peakE = e; peakS = s; }
42+ }
43+ if (peakE === 0) return Math.round(tCoarse * sr);
44+ let s = peakS;
45+ while (s - w >= from) {
46+ const e = env(s - w);
47+ if (e < 0.02 * peakE) break;
48+ if (e > env(s)) break; // rising again: an earlier, separate bump
49+ s -= w;
50+ }
51+ return s;
52+}
src/numbl/execute.tsadded+68−0View file
@@ -0,0 +1,68 @@
1+/**
2+ * Runs a MATLAB-syntax classifier through numbl, with the same interface
3+ * the audio-spike-sorting benchmark uses, so an algorithm developed
4+ * there drops in here unchanged:
5+ *
6+ * function [times, labels] = anyname(x, fs, training)
7+ *
8+ * x is the take (column vector), fs its rate, and training a struct with
9+ * snips ({1 x nVoices} cell of [snipLen x nSnips] matrices), names, and
10+ * pre (samples before the hit onset within each snippet). It returns
11+ * event times in seconds and 1-based voice indices. Imported only by the
12+ * worker and the node test, never by the page, so numbl stays out of the
13+ * main bundle.
14+ */
15+import { executeCode, RTV } from 'numbl';
16+import type { RuntimeValue } from 'numbl';
17+import { extractFunctionName } from './source.ts';
18+import type { Detection, TrainingArrays } from './types.ts';
19+
20+export function runAlgorithm(
21+ source: string,
22+ x: Float32Array | Float64Array,
23+ fs: number,
24+ training: TrainingArrays,
25+): { detections: Detection[]; logs: string[] } {
26+ const fnName = extractFunctionName(source);
27+ if (!fnName) throw new Error('the classifier must define a function [times, labels] = name(x, fs, training)');
28+
29+ const xd = x instanceof Float64Array ? x : Float64Array.from(x);
30+ const trainingStruct = RTV.struct({
31+ snips: RTV.cell(
32+ training.snips.map((s) => RTV.tensor(Float64Array.from(s.data), [s.snipLen, Math.max(s.count, 0)])),
33+ [1, training.snips.length],
34+ ),
35+ names: RTV.cell(training.names.map((n) => RTV.char(n)), [1, training.names.length]),
36+ pre: RTV.num(training.pre),
37+ });
38+
39+ const logs: string[] = [];
40+ const result = executeCode(
41+ `[times__, labels__] = ${fnName}(x, fs, training);\ntimes__ = times__(:); labels__ = labels__(:);`,
42+ {
43+ initialVariableValues: { x: RTV.tensor(xd, [xd.length, 1]), fs: RTV.num(fs), training: trainingStruct },
44+ optimization: '1',
45+ displayResults: false,
46+ onOutput: (text: string) => logs.push(text),
47+ },
48+ [{ name: `${fnName}.m`, source }],
49+ 'main.m',
50+ );
51+
52+ const times = toVector(result.variableValues.times__);
53+ const labels = toVector(result.variableValues.labels__);
54+ if (times.length !== labels.length) {
55+ throw new Error(`times (${times.length}) and labels (${labels.length}) differ in length`);
56+ }
57+ const detections: Detection[] = times.map((t, i) => ({ t, typeIndex: Math.round(labels[i]) - 1 }));
58+ detections.sort((a, b) => a.t - b.t);
59+ return { detections, logs };
60+}
61+
62+function toVector(v: RuntimeValue | undefined): number[] {
63+ if (v === undefined || v === null) throw new Error('the classifier did not return times/labels');
64+ if (typeof v === 'number') return [v];
65+ const t = v as { data?: Float64Array };
66+ if (t.data instanceof Float64Array) return Array.from(t.data);
67+ throw new Error('times/labels must be numeric vectors');
68+}
src/numbl/index.tsadded+18−0View file
@@ -0,0 +1,18 @@
1+/** The shipped classifier source and the starting point for a custom one. */
2+import silenceGapKnnSrc from './silence_gap_knn.m?raw';
3+
4+export const DEFAULT_CLASSIFIER_NAME = 'silence_gap_knn';
5+export const DEFAULT_CLASSIFIER_SOURCE = silenceGapKnnSrc;
6+
7+export const CUSTOM_CLASSIFIER_TEMPLATE = `function [times, labels] = my_classifier(x, fs, training)
8+% x the take, column vector at fs Hz (16 kHz)
9+% training struct: snips {1 x nVoices} cell of [snipLen x nSnips]
10+% matrices (columns are training snippets), names {1 x nVoices},
11+% pre (samples before the hit onset within each snippet)
12+% Return column vectors: hit times in seconds and 1-based voice indices.
13+% The same interface as the audio-spike-sorting benchmark, so an
14+% algorithm developed there drops in here unchanged.
15+times = zeros(0, 1);
16+labels = zeros(0, 1);
17+end
18+`;
src/numbl/silence_gap_knn.madded+131−0View file
@@ -0,0 +1,131 @@
1+function [times, labels] = silence_gap_knn(x, fs, training)
2+% Segment on quiet gaps, then classify with kNN over spectral shape.
3+%
4+% Ported from the audio-spike-sorting benchmark, where it was developed
5+% against synthesized recordings whose background is digitally silent.
6+% One adaptation for live microphone takes: the segmentation threshold
7+% is raised from a fraction of the recording peak to a robust noise
8+% floor estimate (a multiple of the median absolute sample), since a
9+% mic's background is never exactly zero. On digitally silent audio the
10+% median is zero and the original threshold takes over, so benchmark
11+% behavior is unchanged.
12+%
13+% Detection: samples above the threshold are grouped into regions, gaps
14+% of a few milliseconds inside a region are closed, sub-10 ms glitches
15+% are dropped, and a region much longer than one snippet is split into
16+% snippet-length events, which handles back-to-back hits. The event time
17+% is the region start plus the pre-onset offset. Each event is then
18+% classified by distance-weighted k-nearest-neighbours over
19+% level-invariant spectral features of the post-onset segment.
20+%
21+% Overlapping events remain the limitation: simultaneous hits merge into
22+% one region and only the length-based split separates them.
23+nTypes = numel(training.snips);
24+L = size(training.snips{1}, 1);
25+pre = training.pre;
26+n = numel(x);
27+
28+% ---- detection: contiguous above-noise regions ---------------------------
29+eps0 = max(1e-4 * max(abs(x)), 6 * median(abs(x)));
30+gapClose = round(0.002 * fs);
31+minDur = round(0.010 * fs);
32+
33+starts = zeros(0, 1);
34+ends = zeros(0, 1);
35+i = 1;
36+while i <= n
37+ if abs(x(i)) > eps0
38+ s = i;
39+ e = i;
40+ j = i + 1;
41+ quiet = 0;
42+ while j <= n && quiet <= gapClose
43+ if abs(x(j)) > eps0
44+ e = j;
45+ quiet = 0;
46+ else
47+ quiet = quiet + 1;
48+ end
49+ j = j + 1;
50+ end
51+ if e - s + 1 >= minDur
52+ starts(end+1, 1) = s;
53+ ends(end+1, 1) = e;
54+ end
55+ i = e + gapClose + 1;
56+ else
57+ i = i + 1;
58+ end
59+end
60+
61+% split regions covering several back-to-back snippets
62+ev = zeros(0, 1);
63+for r = 1:numel(starts)
64+ nev = max(1, round((ends(r) - starts(r) + 1) / L));
65+ for q = 1:nev
66+ ev(end+1, 1) = starts(r) + (q - 1) * L;
67+ end
68+end
69+
70+% ---- training features --------------------------------------------------
71+nTrain = 0;
72+for k = 1:nTypes
73+ nTrain = nTrain + size(training.snips{k}, 2);
74+end
75+F = zeros(nTrain, 7);
76+Fl = zeros(nTrain, 1);
77+r = 0;
78+for k = 1:nTypes
79+ S = training.snips{k};
80+ for j = 1:size(S, 2)
81+ r = r + 1;
82+ F(r, :) = spectral_features(S(pre+1:end, j), fs);
83+ Fl(r) = k;
84+ end
85+end
86+mu = mean(F, 1);
87+sd = std(F, 0, 1) + 1e-9;
88+Fz = (F - repmat(mu, nTrain, 1)) ./ repmat(sd, nTrain, 1);
89+
90+% ---- classify each event ------------------------------------------------
91+kNN = min(5, nTrain);
92+times = zeros(0, 1);
93+labels = zeros(0, 1);
94+for e = 1:numel(ev)
95+ s = ev(e);
96+ seg = x(min(n, s + pre):min(n, s + L - 1));
97+ f = (spectral_features(seg, fs) - mu) ./ sd;
98+ d = sqrt(sum((Fz - repmat(f, nTrain, 1)).^2, 2));
99+ [ds, di] = sort(d);
100+ votes = zeros(nTypes, 1);
101+ for v = 1:kNN
102+ votes(Fl(di(v))) = votes(Fl(di(v))) + 1 / (ds(v) + 1e-6);
103+ end
104+ [~, lab] = max(votes);
105+ times(end+1, 1) = (s - 1 + pre) / fs;
106+ labels(end+1, 1) = lab;
107+end
108+end
109+
110+function f = spectral_features(seg, fs)
111+% Six log band-energy ratios (mean removed, so absolute level cancels)
112+% and the log spectral centroid.
113+nfft = 2048;
114+m = min(numel(seg), nfft);
115+s2 = seg(1:m);
116+win = 0.5 - 0.5 * cos(2 * pi * (0:m-1)' / max(m - 1, 1));
117+S = fft(s2 .* win, nfft);
118+p = abs(S(1:nfft/2+1)).^2;
119+hz = fs / nfft;
120+edges = [0 200 500 1000 2000 4000 8000];
121+b = zeros(1, 6);
122+for q = 1:6
123+ k0 = max(2, floor(edges(q) / hz) + 1);
124+ k1 = min(nfft/2 + 1, floor(edges(q+1) / hz));
125+ b(q) = log10(sum(p(k0:k1)) + 1e-12);
126+end
127+b = b - mean(b);
128+kv = (0:numel(p)-1)' * hz;
129+cen = sum(p .* kv) / (sum(p) + 1e-12);
130+f = [b, log10(cen + 1)];
131+end
src/numbl/snippets.tsadded+61−0View file
@@ -0,0 +1,61 @@
1+/**
2+ * Turning the voices' training clips into the snippet matrices a numbl
3+ * classifier takes. Each clip is reduced to the classifier rate once,
4+ * each included onset is refined to the foot of its energy rise, and a
5+ * fixed window is cut, exactly as the audio-spike-sorting benchmark cuts
6+ * its snippets. Voices whose clips have no usable audio contribute
7+ * nothing and are reported, since the classifier cannot land hits on
8+ * them; hits learned from takes (which store features, not audio) do not
9+ * reach this path.
10+ */
11+import type { Voice } from '../store.ts';
12+import { downsampleBox, refineOnset } from './dsp.ts';
13+import { CLF_FS, CLF_PRE, CLF_POST, type TrainingArrays } from './types.ts';
14+
15+export interface VoiceTraining {
16+ training: TrainingArrays;
17+ /** Voice id per training type index (label k maps to voiceIds[k-1]). */
18+ voiceIds: string[];
19+ /** Names of voices that contributed no snippets. */
20+ skipped: string[];
21+}
22+
23+export async function buildVoiceTraining(
24+ voices: Voice[],
25+ getClipAudio: (id: string) => Promise<{ samples: Float32Array; sr: number } | null>,
26+): Promise<VoiceTraining> {
27+ const snipLen = CLF_PRE + CLF_POST;
28+ const perVoice: { voice: Voice; snips: Float32Array[] }[] = [];
29+ for (const voice of voices) {
30+ const snips: Float32Array[] = [];
31+ for (const clip of voice.clips) {
32+ const audio = await getClipAudio(clip.id);
33+ if (!audio) continue;
34+ const x = downsampleBox(audio.samples, audio.sr, CLF_FS);
35+ const excluded = new Set(clip.excluded);
36+ clip.onsets.forEach((t, i) => {
37+ if (excluded.has(i)) return;
38+ const s = refineOnset(x, CLF_FS, t);
39+ if (s - CLF_PRE < 0 || s + CLF_POST > x.length) return;
40+ snips.push(x.slice(s - CLF_PRE, s + CLF_POST));
41+ });
42+ }
43+ perVoice.push({ voice, snips });
44+ }
45+
46+ const contributing = perVoice.filter((p) => p.snips.length > 0);
47+ const training: TrainingArrays = {
48+ names: contributing.map((p) => p.voice.name),
49+ pre: CLF_PRE,
50+ snips: contributing.map((p) => {
51+ const data = new Float32Array(snipLen * p.snips.length);
52+ p.snips.forEach((s, j) => data.set(s, j * snipLen));
53+ return { data, snipLen, count: p.snips.length };
54+ }),
55+ };
56+ return {
57+ training,
58+ voiceIds: contributing.map((p) => p.voice.id),
59+ skipped: perVoice.filter((p) => p.snips.length === 0).map((p) => p.voice.name),
60+ };
61+}
src/numbl/source.tsadded+5−0View file
@@ -0,0 +1,5 @@
1+/** Name of the first function defined in a .m source, or null for a plain script. */
2+export function extractFunctionName(source: string): string | null {
3+ const m = source.match(/^\s*function\s+(?:\[[^\]]*\]\s*=\s*|\w+\s*=\s*)?([A-Za-z]\w*)\s*\(/m);
4+ return m ? m[1] : null;
5+}
src/numbl/types.tsadded+23−0View file
@@ -0,0 +1,23 @@
1+/** Shared classifier types, free of any numbl import. */
2+
3+/** The internal rate classification runs at; the take and the training
4+ * snippets are both reduced to this, matching the audio-spike-sorting
5+ * benchmark, so algorithms transfer between the two apps unchanged. */
6+export const CLF_FS = 16000;
7+/** Snippet window around each training onset at CLF_FS (20 ms / 180 ms). */
8+export const CLF_PRE = Math.round(0.02 * CLF_FS);
9+export const CLF_POST = Math.round(0.18 * CLF_FS);
10+
11+export interface Detection {
12+ /** Event time in seconds. */
13+ t: number;
14+ /** 0-based index into the trained-voice list. */
15+ typeIndex: number;
16+}
17+
18+export interface TrainingArrays {
19+ names: string[];
20+ pre: number;
21+ /** Per voice: column-major [snipLen x count] snippet matrix. */
22+ snips: { data: Float32Array; snipLen: number; count: number }[];
23+}
src/store.tsmodified+17−1View file
@@ -103,8 +103,24 @@ export function saveUi(ui: UiState): void {
103103 localStorage.setItem(KEY_UI, JSON.stringify(ui));
104104 }
105105
106+/** How takes are classified: the shipped numbl algorithm (default, its
107+ * source editable), a custom numbl algorithm, or the original TS
108+ * feature-kNN. Null sources mean "the shipped default" / "the template". */
109+export interface ClassifierState {
110+ method: 'builtin' | 'custom' | 'legacy';
111+ builtinSrc: string | null;
112+ customSrc: string | null;
113+}
114+const KEY_CLF = 'instrawave-compose/classifier';
115+export function loadClassifier(): ClassifierState {
116+ return read(KEY_CLF) ?? { method: 'builtin', builtinSrc: null, customSrc: null };
117+}
118+export function saveClassifier(c: ClassifierState): void {
119+ localStorage.setItem(KEY_CLF, JSON.stringify(c));
120+}
121+
106122 export function clearStore(): void {
107- for (const k of [KEY, KEY_IW, KEY_TAKE, KEY_UI]) localStorage.removeItem(k);
123+ for (const k of [KEY, KEY_IW, KEY_TAKE, KEY_UI, KEY_CLF]) localStorage.removeItem(k);
108124 }
109125
110126 // ---- bulk data in IndexedDB ---------------------------------------------------