Course Overview
TOPThe AI+ Audio Practitioner™ course brings sound innovation to life by showing how AI is redefining communication, creativity, and accessibility. Participants explore how AI enhances audio clarity, powers real-time transcription, personalizes listening experiences, and even generates music. Learners experience firsthand how AI transforms raw sound into actionable insights, immersive audio, and intelligent voice-driven interactions. Case studies from healthcare, entertainment, and assistive technologies showcase how AI reshapes the way industries listen, create, and connect. This course provides a dynamic, story-driven journey into the future of intelligent audio systems.
Scheduled Classes
TOP| 07/28/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 08/25/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 09/29/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 10/27/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 11/24/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 12/22/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand core concepts of digital audio processing and AI-driven sound analysis
- Build and apply machine learning models for speech recognition, noise reduction, and audio enhancement
- Develop TTS, voice cloning, and emotion-detection pipelines using deep learning architectures
- Use modern APIs and frameworks to implement audio AI solutions across real-world applications
- Evaluate ethical, privacy, fairness, and security challenges associated with AI-based audio systems
Outline
TOPIntroduction to AI and Sound
- What is AI?
- AI in Daily Life: Audio Examples
- Basics of Sound Waves, Amplitude, Frequency
- Digital Audio Fundamentals
Harnessing AI Across Audio Domains
- AI for Audio Enhancement and Restoration
- AI for Audio Accessibility and Personalization
- AI in Speech and Voice Technologies
- Popular Audio Libraries: Librosa, PyAudio
- Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
- Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
- Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
Machine Learning & AI for Audio
- Machine Learning Models for Audio Applications
- Deep Learning & Advanced AI Techniques for Audio
- Audio-Specific Architectures: CNNs, RNNs, Transformers
- Transfer Learning in Audio AI
- Use Case: Speech-to-Text Transcription for Medical Records
- Case Study: AI-powered Music Generation with Deep Learning
- Hands-on: Build a Speech-to-Text Model Using TensorFlow
Speech Recognition & Text-to-Speech
- Fundamentals of Speech Recognition & Phonetics
- API-based ASR Solutions
- Building Custom ASR Models with Transformers
- Introduction to TTS & Voice Cloning
- Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
- Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
- Hands-on: Transcribe audio with an ASR API; generate speech from text
Audio Enhancement & Noise Reduction
- Common Audio Issues
- AI-based Noise Filtering & Enhancement
- Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
- Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
- Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
Prerequisites
TOPRequired
- Basic programming knowledge – Familiarity with Python or similar languages
- Understanding of audio signal processing – Know fundamental audio manipulation techniques
- Machine learning fundamentals – Basic knowledge of algorithms and model training
- Mathematical proficiency – Comfort with linear algebra and probability concepts
- Experience with audio software tools – Hands-on use of DAWs or similar tool
