Audio Fingerprinting Library
A robust Go implementation of an audio fingerprinting system that generates distinctive fingerprints from audio files, inspired by the Shazam algorithm.
Overview
This project implements a complete audio fingerprinting system in Go. It processes audio files to generate compact, robust fingerprints that can uniquely identify audio even in the presence of noise or distortion. The system works by analyzing the time-frequency domain of audio signals, detecting constellation points of audio peaks, and creating hash pairs that form the fingerprint. The implementation includes complete audio processing, digital signal processing, and the core fingerprinting algorithm.
Challenges
Implementing robust audio preprocessing to handle various input formats and noise conditions
Creating an efficient peak detection algorithm in the frequency domain
Developing a hash generation system that produces compact yet distinctive fingerprints
Ensuring the fingerprinting is resistant to audio transformations like pitch shifts and time stretching
Optimizing performance for processing large audio files efficiently
Solutions
Applied low-pass filtering and downsampling to normalize and optimize audio input
Implemented a band-based peak detection algorithm to identify distinctive features in the spectrogram
Created a pairing algorithm that generates 32-bit hashes containing frequency and time information
Used parallel processing with goroutines to compute the spectrogram efficiently
Developed comprehensive test suites to verify algorithm correctness and robustness
Results
Built a fully functional audio fingerprinting system in pure Go
Created fingerprints that are robust against common audio distortions
Achieved efficient performance through optimized DSP implementations
Developed a well-structured codebase with thorough test coverage
Implemented a Shazam-inspired constellation algorithm with proven effectiveness in audio identification