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
