AI+ Audio

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

1. 1

   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
2. 2

   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
3. 3

   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
4. 4

   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
5. 5

   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
6. 6

   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
7. 7

   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
8. 8

   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