Audio Fingerprinting
Custom AI that listens
Not just Shazam. We train custom neural models on YOUR audio corpus. Identify tracks, isolate stems, detect samples—built for your specific use case.
Representation LearningContrastive LearningVector SearchModel Training PipelinesScalable ML InfrastructureGCP
The Problem
Off-the-shelf audio ID fails on remixes, covers, stems, or niche catalogs. You need fingerprinting that understands your specific audio domain.
The Magic
We train custom neural networks on your audio corpus. Your model learns your music—the quirks, the production styles, the metadata you care about. Identification that no generic API can match.
The Tech
- MuQ encoder architecture (custom developed)
- Contrastive learning on spectral features
- Trained on your data, deployed in your pipeline