Audio Processing Basics – Hosted by Nathan Rigoni

In this episode of The Phront Room we dive into the world of sound, breaking down how raw audio waves become the speech‑to‑text and text‑to‑speech systems we use every day. From the historic phonograph to modern wake‑word assistants, we explore the science behind capturing pressure changes in air and turning them into meaningful symbols. What if you could teach a tiny model on a smartwatch to hear a single “help” call inside a burning building?

What you will learn

• How sound is represented as a time‑series waveform (the “signal”).
• The difference between signals and symbols (phonemes) in audio models.
• How speech‑to‑text models learn statistical variations of pronunciation from millions of samples (e.g., Librivox recordings).
• The reverse process: converting text back into audio via phoneme generation.
• Real‑world edge‑AI use cases, such as wake‑word detection on phones, watches, and drones for firefighting safety.

Resources mentioned

• Librivox open‑source audio books – https://librivox.org
• Mozilla DeepSpeech (open‑source speech‑to‑text) – https://github.com/mozilla/DeepSpeech
• OpenAI Whisper (robust speech transcription) – https://github.com/openai/whisper
• Google Assistant / Siri wake‑word technology (commercial examples).
• Research on phoneme‑based TTS models (e.g., Tacotron, WaveNet).

Why this episode matters
Understanding audio processing gives you the toolkit to build smarter, smaller AI that can act on sound in real time. Whether you’re creating a personal assistant, a voice‑controlled robot, or a life‑saving rescue drone, the principles covered here form the foundation for any application that turns vibration into insight.

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Keywords
audio processing, speech‑to‑text, text‑to‑speech, phonemes, wake word, edge AI, small models, drones, firefighting technology, machine learning, signal vs. symbol.