AI+ Audio Practitioner™

Course structure

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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