Voice Quality Metrics: Jitter, Shimmer, HNR, and AVQI Explained
Clinical voice quality metrics demystified: jitter, shimmer, HNR, and AVQI with normal ranges, Praat extraction, and Python code for speech-language pathology pipelines.
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Notes & perspectives
Notes techniques et retours de pratique sur le traitement du signal, l’IA et le développement logiciel.
Articles en anglais
dB SPL vs dBFS: Understanding Audio Levels for Engineers and Clinicians
dB SPL vs dBFS explained: conversion formula, calibration workflow, and why the distinction matters for clinical voice analysis and audio ML pipelines.
Lire l’articleFine-Tuning Audio ML Models: Plan, Audit, Diagnose
A structured framework for audio ML fine-tuning: how to plan a new pipeline, audit an existing script, and diagnose an underperforming model.
Lire l’articleMLOps Setup – OVHcloud + DVC + MLflow (Audio Dataset)
A practical guide to a reproducible MLOps pipeline for audio data: OVHcloud Object Storage, DVC for dataset versioning, and MLflow for tracking.
Lire l’articleThe Silent Sentinels: When Materials Speak, Quality Control Listens
Four unexpected uses of passive acoustic monitoring in manufacturing: from 3D-printed concrete to cryogenic aerospace composites, materials speak through sound.
Lire l’articleWav2Vec2 & XLSR Model Guide
A comprehensive guide to Wav2Vec2 model variants, their use cases, and best practices for speech processing tasks.
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