Audio Professional · Music Producer · AI Audio/Video Annotator
📍 Buenos Aires, Argentina · Open to remote projects and opportunities
I combine 25+ years of professional experience in music and audio with hands-on work in AI data annotation, transcription quality review, and Python-based audio analysis.
My focus is on accurate listening, musical detail, clear documentation, and quality control.
| Professional audio & music | AI data & quality review |
|---|---|
| Live sound and studio recording | Audio/video annotation |
| Mixing and mastering | Speech transcription QA |
| Music production and arrangements | Speaker labeling and timestamps |
| Instrumentation, harmony, and structure analysis | Audio classification and guideline-based review |
- Python: practical scripts for audio inspection and analysis
- Audio: digital audio processing, critical listening, WAV inspection
- AI & audiovisual tools: OpenAI Whisper, FFmpeg, OpenCV, PySceneDetect
- Annotation: music structure, instrumentation, transcription corrections, segment timing, and quality checks
I'm actively developing my Python skills and expanding my public portfolio. My production and annotation experience is extensive; my software projects are documented with their current scope and limitations.
A Python-based audio analysis portfolio.
Published Python modules:
- WAV Audio Quality Analyzer — checks PCM WAV duration, channel count, bit depth, sample peak, RMS, and near-clipping counts.
- Music Annotation QA & CSV Export — validates manually labeled musical sections, timestamps, instruments, and other annotation fields; exports CSV.
- Audio Timeline Dynamics — measures short-window RMS, decoded sample peaks, quiet intervals, and changes in level in MP3/WAV audio.
- Acoustic Transition Candidates (experimental) — proposes listening points where sound statistics change. It does not automatically label verses or choruses.
The tools include synthetic automated tests. MP3 decoding and the transition detector require FFmpeg.
In progress: AI-assisted transcription and audiovisual analysis workflows. These are not yet presented as completed published modules.
Explore the code and validation results →
Music & Audio AI · Audio Quality Assurance · Music Annotation · Transcription QA · Audio Dataset Evaluation · Python Audio Tools
Public examples only. No confidential client recordings or annotation datasets are shared here.