Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

Official implementation of Neural Audio Fingerprint (ICASSP 2021)

Experimental Audio Search Algorithm Implementation (Shazam demo)

On-device Speech-to-Index engine powered by deep learning

This project use PANNs for audio tagging and sound event detection, and finally get audio embeddings. Then Milvus is used to search the similarity audio items.

🎵 Audio Fingerprinting Microservice | Shazam-like System A scalable microservice for audio recognition and fingerprinting, implementing Shazam-like functionality using: - Spring Boot backend - Elasticsearch for fast audio pattern matching - Docker containerization - Kafka for event streaming - MinIO for audio storage

Deep Learning for Audio in CSharp and Keras

simple tool for quickly searching and managing sound effects.

Self-hosted semantic audio search engine powered by CLAP + FastAPI + Qdrant + Gradio. Search and tag sound effects or music by meaning — not just filenames.

Audio Deep Learning Project in Java

AudioFingerprint is a production-ready, local audio fingerprinting and song identification system inspired by Shazam and Google Sound Search. It uses spectral peak extraction and combinatorial hashing to identify songs from short audio clips in milliseconds, with a clean Python + Flask architecture suitable for real-world deployments.

Song identification combining landmark audio fingerprinting with CNN/AST/MERT embeddings (contrastive InfoNCE), benchmarked on Jamendo with strong noise robustness.

🗄️ Run a self-hosted Qdrant vector database using Docker with easy setup, persistent data, and integration for smooth service connections.