AI+ Audio

Hours: 8 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 8
Retail Price: $195.00

Course Overview:

This course offers a beginner-friendly yet comprehensive journey into the world of AI-powered audio, empowering you to master speech processing, sound enhancement, and voice synthesis through practical, hands-on frameworks. You will gain industry-ready expertise by exploring how these transformative technologies are reshaping music, media, and communication. By the end of the program, you’ll be equipped to creatively apply AI tools to analyze, optimize, and innovate within the evolving audio landscape.

Recommended Prerequisites:

• 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 tools.

Course Outline:

Lesson 1: Introduction to AI and Sound

• 1.1 What is AI?
• 1.2 AI in Daily Life: Audio Examples
• 1.3 Basics of Sound Waves, Amplitude, Frequency
• 1.4 Digital Audio Fundamentals

Lesson 2: Harnessing AI Across Audio Domains

• 2.1 AI for Audio Enhancement and Restoration
• 2.2 AI for Audio Accessibility and Personalization
• 2.3 AI in Speech and Voice Technologies
• 2.4 Popular Audio Libraries: Librosa, PyAudio
• 2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
• 2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
• 2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform

Lesson 3: Machine Learning & AI for Audio

• 3.1 Machine Learning Models for Audio Applications
• 3.2 Deep Learning & Advanced AI Techniques for Audio
• 3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
• 3.4 Transfer Learning in Audio AI
• 3.5 Use Case: Speech-to-Text Transcription for Medical Records
• 3.6 Case Study: AI-powered Music Generation with Deep Learning
• 3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow

Lesson 4: Speech Recognition & Text-to-Speech

• 4.1 Fundamentals of Speech Recognition & Phonetics
• 4.2 API-based ASR Solutions
• 4.3 Building Custom ASR Models with Transformers
• 4.4 Introduction to TTS & Voice Cloning
• 4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
• 4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
• 4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text

Lesson 5: Audio Enhancement & Noise Reduction

• 5.1 Common Audio Issues
• 5.2 AI-based Noise Filtering & Enhancement
• 5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
• 5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
• 5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio

Lesson 6: Emotion & Sentiment Detection from Audio

• 6.1 Introduction to Emotion Detection
• 6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
• 6.3 Challenges: Bias, Multilingual Contexts, Reliability
• 6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
• 6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
• 6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples

Lesson 7: Ethical and Privacy Considerations

• 7.1 Deepfakes and Voice Cloning Risks
• 7.2 Privacy and Data Security
• 7.3 Bias and Fairness in Audio AI
• 7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
• 7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
• 7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist

Lesson 8: Advanced Applications & Future Trends

• 8.1 Sound Event Detection & Classification
• 8.2 Audio Search and Indexing
• 8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
• 8.4 Emerging Careers in Audio AI

All necessary course materials are included.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.