SoundSleuth

Tech Stack

Python

Node

Java

React

javascript

Description

SoundSleuth is an offline music recognition system designed to identify songs from short audio clips or live recordings using audio fingerprinting. Unlike commercial alternatives, it is fully open-source and platform-independent. The system extracts unique audio fingerprints using frequency peaks and matches them against a local database of known songs. Built using Python for signal processing and Golang for the backend, it delivers fast and accurate results without needing internet connectivity. The project addresses the need for transparent and customizable song recognition tools useful for music lovers, content creators, and developers.

Progress During Hackathon

During Hackaccino 3.0, we successfully built a working prototype of SoundSleuth, a music recognition system capable of identifying songs from short audio clips. We implemented a music fingerprinting algorithm in Python to extract audio features and matched them using a Golang backend server. Our team integrated audio input processing, database management for song fingerprints, and a basic web interface for user interaction. We also developed CLI tools for testing and managing the song database. By the end of the hackathon, the system was able to identify songs offline using pre-saved fingerprints with high accuracy, showcasing the core functionality of the app.

Fundraising Status

Currently, SoundSleuth is a self-funded open-source project developed during Hackaccino 3.0. We have not raised any external funding yet, but we are open to future collaborations, grants, or sponsorships to scale the project and expand its database and features.