This project is not ready for production, and it is still under development.
🎧 Sound pattern recognition library.
Features:
- Match a sample from a local file.
- Match a sample from a sound stream (Now only mp3 radio stream).
Strategies:
- Use a fingerprint hashing mechanism based on Fast Fourier Transform and Accustic Fingerprint Hash.
- Focus on parallel stream chunk fingerprint hashing method.
- Try to offer a simple way to match a given sample against songs in database collection.
- Provide a built-in solution to handle fingerprint storage.
This library offers Redis database support for storing
hashes and finding matches. By using
trait Repository, it is fairly easy to implement this feature in any database. Using a database engine that supports fast hash lookup is highly recommended. The best performance can be achieved on a fairly simple schema within database RAM. - Use async stream request and parallel stream hashing. There is no need to build a parallel mechanism by yourself. The stream listener collects chunks asynchronously and decodes them in a separate thread, then pipes it to the receiver. The receiver is boxed by atomic type, so hashing and matching can be done in a separate thread than the listener. This design allows more processor resources to be used, and speeds up the matching algorithm significantly.
Improvements:
- The weakest point so far is that only one thread can read and write to the database. The main problem is that the hash lookup wasn't showing any significant performance improvement when using parallel threads. Additionally, when the database size increases, parallel calls make hash lookups take longer. This factor made me stick to single-threaded database calls. (If you know how to improve this, please provide your solution or issue a proposal ticket).
- Test against self cached file samples
- Run local redis database with
systemctl start redis-server.service - Test it with
cargo test -- --nocaptureorcargo test<- with less information
- Building. Please build with
RUSTFLAGS="--emit=asm"flag enabled, which forces the compiler to use a single LLVM module for the entire crate, allowing better LLVM optimization.
🌸 Dla Grzechotki 🍒