Articles on signal processing, AI, and audio technology
Clinical voice quality metrics demystified: jitter, shimmer, HNR, and AVQI with normal ranges, Praat extraction, and Python code for speech-language pathology pipelines.
Read more →dB SPL vs dBFS explained: conversion formula, calibration workflow, and why the distinction matters for clinical voice analysis and audio ML pipelines.
Read more →A structured framework for audio ML fine-tuning: how to plan a new pipeline, audit an existing script, and diagnose an underperforming model.
Read more →A practical guide to a reproducible MLOps pipeline for audio data: OVHcloud Object Storage, DVC for dataset versioning, and MLflow for tracking.
Read more →Four unexpected uses of passive acoustic monitoring in manufacturing: from 3D-printed concrete to cryogenic aerospace composites, materials speak through sound.
Read more →A comprehensive guide to Wav2Vec2 model variants, their use cases, and best practices for speech processing tasks.
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