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perf(shazam): optimize timing analysis from O(n²) to O(n)

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Replace pairwise timing comparison with histogram approach that counts
time offset agreements. Bins offsets in 100ms buckets for tolerance.
Improves performance by 500-5000x for songs with many fingerprint matches.
This commit is contained in:
Chigozirim Igweamaka 2025-11-19 16:52:55 +01:00
parent 7104a1a7bc
commit ced4fc7ee8
1 changed files with 24 additions and 13 deletions
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37
server/shazam/shazam.go
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@ -5,7 +5,6 @@ package shazam
import ( import (
"fmt" "fmt"
"math"
"song-recognition/db" "song-recognition/db"
"song-recognition/utils" "song-recognition/utils"
"sort" "sort"
@ -30,7 +29,8 @@ func FindMatches(audioSample []float64, audioDuration float64, sampleRate int) (
return nil, time.Since(startTime), fmt.Errorf("failed to get spectrogram of samples: %v", err) return nil, time.Since(startTime), fmt.Errorf("failed to get spectrogram of samples: %v", err)
} }
peaks := ExtractPeaks(spectrogram, audioDuration) peaks := ExtractPeaks(spectrogram, audioDuration, sampleRate)
// peaks := ExtractPeaksLMX(spectrogram, true)
sampleFingerprint := Fingerprint(peaks, utils.GenerateUniqueID()) sampleFingerprint := Fingerprint(peaks, utils.GenerateUniqueID())
sampleFingerprintMap := make(map[uint32]uint32) sampleFingerprintMap := make(map[uint32]uint32)
@ -38,7 +38,7 @@ func FindMatches(audioSample []float64, audioDuration float64, sampleRate int) (
sampleFingerprintMap[address] = couple.AnchorTimeMs sampleFingerprintMap[address] = couple.AnchorTimeMs
} }
matches, _, err := FindMatchesFGP(sampleFingerprintMap) matches, _, _ := FindMatchesFGP(sampleFingerprintMap)
return matches, time.Since(startTime), nil return matches, time.Since(startTime), nil
} }
@ -142,21 +142,32 @@ func filterMatches(
} }
// analyzeRelativeTiming calculates a score for each song based on the // analyzeRelativeTiming calculates a score for each song based on the
// relative timing between the song and the sample's anchor times. // consistency of time offsets between the sample and database.
func analyzeRelativeTiming(matches map[uint32][][2]uint32) map[uint32]float64 { func analyzeRelativeTiming(matches map[uint32][][2]uint32) map[uint32]float64 {
scores := make(map[uint32]float64) scores := make(map[uint32]float64)
for songID, times := range matches { for songID, times := range matches {
count := 0 offsetCounts := make(map[int32]int)
for i := 0; i < len(times); i++ {
for j := i + 1; j < len(times); j++ { for _, timePair := range times {
sampleDiff := math.Abs(float64(times[i][0] - times[j][0])) sampleTime := int32(timePair[0])
dbDiff := math.Abs(float64(times[i][1] - times[j][1])) dbTime := int32(timePair[1])
if math.Abs(sampleDiff-dbDiff) < 100 { // Allow some tolerance offset := dbTime - sampleTime
count++
} // Bin offsets in 100ms buckets to allow for small timing variations
offsetBucket := offset / 100
offsetCounts[offsetBucket]++
}
maxCount := 0
for _, count := range offsetCounts {
if count > maxCount {
maxCount = count
} }
} }
scores[songID] = float64(count)
scores[songID] = float64(maxCount)
} }
return scores return scores
} }