Advanced Audio MIR & ML Pipeline for DJ Metadata Export
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About this role
XML, Python, Audio Services, Machine Learning (ML), C++ Programming, Audio Processing, Digital Signal Processing, Sound Engineering, JUCE, AI Model Development · Looking to hire an experienced Audio DSP / Machine Learning Engineer to build an AI-driven Music Information Retrieval (MIR) engine. The system will extract production-focused audio features from finished tracks and export them directly into Rekordbox-compatible metadata. Beyond basic tempo and key, the system must leverage Machine Learning models and DSP to automatically calculate deeper sound engineering metrics—sub-bass pressure, full-spectrum RMS density, transient impact, and dynamic punch—and categorize tracks based on acoustic weight and structural energy. Scope of Work: Develop an autonomous pipeline/script (Python using Librosa/Essentia/PyTorch, or C++/JUCE) that ingests WAV, AIFF, and MP3 files. Automatically extract BPM, musical key, and advanced production metrics (sub-bass vs. kick, RMS density, transients, dynamic punch). Detect structural shifts (drops, breakdowns, high-energy sections) and calculate macro/micro energy levels automatically. Export analysis results into a fully valid Rekordbox XML structure (or ID3 tags) ready for direct library import. Acceptance Criteria: The developer will run the engine on a sample set of audio files. The system must autono