---
title: "An Industrial-Strength Audio Search Algorithm"
description: "This is the paper behind Shazam: how a phone can name a song from ten noisy seconds in a pub. Read it to see how a track is reduced to a constellation of spectrogram peaks, how pairing peaks into 32-bit hashes makes lookup about ten thousand times faster, and why a match is declared only when the surviving hashes agree on one time offset."
canonical_url: "https://fanout.sh/daily/2026-08-19-shazam-audio-search"
md_url: "https://fanout.sh/daily/2026-08-19-shazam-audio-search.md"
last_updated: "2026-08-19"
access: "public"
---

# An Industrial-Strength Audio Search Algorithm

This is the paper behind Shazam: how a phone can name a song from ten noisy seconds in a pub. Read it to see how a track is reduced to a constellation of spectrogram peaks, how pairing peaks into 32-bit hashes makes lookup about ten thousand times faster, and why a match is declared only when the surviving hashes agree on one time offset.

## Paper details

- Authors: Avery Li-Chun Wang

- Venue: ISMIR 2003

- Track: Systems

- Difficulty: Easy

- Reading time: 30 min

- Original paper: https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf

## What you will learn

- Why spectrogram peaks survive shouting, traffic, and phone-codec compression when almost every other audio feature is destroyed.

- How pairing an anchor peak with nearby peaks turns 10-bit features into 30-bit hashes, trading about 10 times the storage for about 10,000 times the search speed.

- Why a histogram of sample-to-track time offsets can confirm a match with only about 1-2% of the fingerprint tokens surviving the noise.

## Continue exploring

[Explore inverted indexes](https://fanout.sh/system/archive/inverted-index-and-posting-lists): Continue into Fanout's inverted-index guide and connect Shazam's sorted hash-token index to posting lists, exact-match lookup, and the read paths of text search engines.

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