Files
owenandClaude Sonnet 5 b6acd63c2d Implement sonicSimilarity extension with real audio feature analysis
Adds internal/audiofeatures: decodes each scanned song to PCM via
ffmpeg and computes actual measured characteristics — tempo (via
autocorrelation of the frame energy envelope, left unset rather than
guessed when a clip is too short to contain a beat), an approximate
loudness figure, spectral centroid/rolloff and zero-crossing rate (via
a hand-written FFT), and a 12-bin chroma vector used both for a
Krumhansl-Kessler key estimate and as similarity-index input. No
external DSP/ML dependency.

The scanner now runs this analysis after indexing each song (as part
of the already-background scan, not on any request path) and stores
results in the existing audio_features table via the new
database.UpsertAudioFeatures/GetAudioFeatures/ListAllAudioFeatures.

internal/similarity is a small in-process nearest-neighbor index:
z-score normalizes each feature dimension across the analyzed library
and answers Nearest (ranked real distances) and Path (a feature-space
straight-line walk from one track to another, hopping to the closest
not-yet-used real song at each step — never a fabricated intermediate
track).

getSonicSimilarTracks and findSonicPath expose these through the
standard sonicMatch response, converting distance to a normalized
(0,1] similarity score. A song that hasn't been analyzed yet is simply
absent from results rather than faked.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014r98AtcBbevpscWLsfwVvv
2026-08-23 08:30:20 -05:00

117 lines
3.4 KiB
Go

package similarity
import "testing"
func tempoLibrary() []Item {
// A synthetic library where the only varying dimension is "tempo":
// songs at 60, 70, 80, ..., 150 BPM. Deterministic and needs no
// audio decoding, so it exercises the index math directly.
var items []Item
for i, bpm := range []float64{60, 70, 80, 90, 100, 110, 120, 130, 140, 150} {
items = append(items, Item{ID: idFor(i), Vector: []float64{bpm, 0, 0}})
}
return items
}
func idFor(i int) string {
return string(rune('a' + i))
}
func TestNearestOrdersByActualDistance(t *testing.T) {
idx := Build(tempoLibrary())
// "d" is 90 BPM; its closest neighbors by tempo should be "c" (80)
// and "e" (100), in that order (tie broken by index order is fine
// since both are exactly 10 BPM away — check the pair, not order).
matches := idx.Nearest("d", 2)
if len(matches) != 2 {
t.Fatalf("matches = %v, want 2", matches)
}
got := map[string]bool{matches[0].ID: true, matches[1].ID: true}
if !got["c"] || !got["e"] {
t.Errorf("nearest to d(90bpm) = %v, want {c(80bpm), e(100bpm)}", matches)
}
}
func TestNearestExcludesSelf(t *testing.T) {
idx := Build(tempoLibrary())
matches := idx.Nearest("a", 20)
for _, m := range matches {
if m.ID == "a" {
t.Fatal("Nearest must never include the query track itself")
}
}
}
func TestNearestUnknownID(t *testing.T) {
idx := Build(tempoLibrary())
if matches := idx.Nearest("does-not-exist", 5); matches != nil {
t.Errorf("expected nil for unknown id, got %v", matches)
}
}
func TestNearestFarthestIsOppositeEnd(t *testing.T) {
idx := Build(tempoLibrary())
matches := idx.Nearest("a", 20) // a = 60bpm, all 9 others
if len(matches) != 9 {
t.Fatalf("matches = %d, want 9", len(matches))
}
farthest := matches[len(matches)-1]
if farthest.ID != "j" { // j = 150bpm, the farthest from 60bpm
t.Errorf("farthest from a(60bpm) = %v, want j(150bpm)", farthest)
}
}
func TestPathIsMonotonicAlongTheOnlyVaryingDimension(t *testing.T) {
idx := Build(tempoLibrary())
path := idx.Path("a", "j", 5) // 60bpm -> 150bpm in 5 hops
if len(path) != 5 {
t.Fatalf("path = %v, want length 5", path)
}
if path[0] != "a" || path[len(path)-1] != "j" {
t.Fatalf("path endpoints = %q..%q, want a..j", path[0], path[len(path)-1])
}
seen := map[string]bool{}
for _, id := range path {
if seen[id] {
t.Fatalf("path repeats %q: %v", id, path)
}
seen[id] = true
}
// With tempo as the only varying dimension, a straight-line
// interpolation from 60 to 150 BPM should visit strictly increasing
// tempos.
bpmOf := map[string]float64{"a": 60, "b": 70, "c": 80, "d": 90, "e": 100, "f": 110, "g": 120, "h": 130, "i": 140, "j": 150}
for i := 1; i < len(path); i++ {
if bpmOf[path[i]] <= bpmOf[path[i-1]] {
t.Errorf("path not monotonically increasing in tempo: %v", path)
break
}
}
}
func TestPathUnknownEndpoint(t *testing.T) {
idx := Build(tempoLibrary())
if path := idx.Path("a", "nope", 5); path != nil {
t.Errorf("expected nil path for unknown endpoint, got %v", path)
}
}
func TestBuildEmpty(t *testing.T) {
idx := Build(nil)
if matches := idx.Nearest("a", 5); matches != nil {
t.Errorf("expected nil from an empty index, got %v", matches)
}
}
func TestBuildConstantDimensionDoesNotDivideByZero(t *testing.T) {
items := []Item{
{ID: "x", Vector: []float64{5, 1}},
{ID: "y", Vector: []float64{5, 2}},
}
idx := Build(items)
matches := idx.Nearest("x", 5)
if len(matches) != 1 || matches[0].ID != "y" {
t.Fatalf("matches = %v", matches)
}
}