Deep Learning Specialization · Sequence Models

Trigger Word Detection

A wake-word detection project that synthesizes 10-second audio examples, converts them to spectrograms, and trains a Conv1D-GRU model to detect “activate.”

Audio preprocessingSpectrogramsConv1DGRU sequence modelingTime-distributed prediction

GitHub

What this project is about

Trigger word detection brings sequence modeling into audio. The project synthesizes examples, computes spectrograms, and trains Conv1D/GRU layers to output a probability timeline for “activate.”

Implementation focus

The lesson was that NLP-style sequence thinking also applies to signals: timing, local patterns, and labels across time are central.

Key results and artifacts

Raw audio length: 441,000 time steps
Spectrogram model input: 5,511×101
Model parameters: 523,329
Output time steps: 1,375

What I learned

This project expanded sequence learning beyond text into audio signals and time-localized detection.

Milestone deep dive

What this project proves in my learning path.

A wake-word detection project that synthesizes 10-second audio examples, converts them to spectrograms, and trains a Conv1D-GRU model to detect “activate.” The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerimages, anchors, boxes, and class scores

Model layerYOLO-style detection pipeline with filtering and NMS

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerlocalized objects with confidence scores

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Audio preprocessingSpectrogramsConv1DGRU sequence modelingTime-distributed prediction

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This project expanded sequence learning beyond text into audio signals and time-localized detection.

Important outputs and evidence

Raw audio length: 441,000 time steps
Spectrogram model input: 5,511×101
Model parameters: 523,329
Output time steps: 1,375

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.