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AI+ Audio™

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  • Course level: Intermediate

About Course

Experience the power of AI in Audio™ to reinvent music production, elevate sound design, and craft immersive auditory experiences.

Description

About This Course

  • Empower Audio Innovation with AI: Creative, Practical, Transformative
  • Beginner-Friendly Learning: Perfect for newcomers eager to explore AI-powered audio, covering essential concepts with ease
  • Comprehensive Skill Building: Includes speech processing, sound enhancement, voice synthesis, and real-world audio AI applications
  • Industry-Ready Expertise: Understand how AI is reshaping music, media, entertainment, and communication sectors
  • Hands-On Direction: Provides practical frameworks and guided exercises to help you create, analyse, and optimise audio using AI

Course Modules

1

Module 1: Introduction to AI and Sound

  1. 1.1 What is AI?
  2. 1.2 AI in Daily Life: Audio Examples
  3. 1.3 Basics of Sound Waves, Amplitude, Frequency
  4. 1.4 Digital Audio Fundamentals
2

Module 2: Harnessing AI Across Audio Domains

  1. 2.1 AI for Audio Enhancement and Restoration
  2. 2.2 AI for Audio Accessibility and Personalization
  3. 2.3 AI in Speech and Voice Technologies
  4. 2.4 Popular Audio Libraries: Librosa, PyAudio
  5. 2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
  6. 2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
  7. 2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
3

Module 3: Machine Learning & AI for Audio

  1. 3.1 Machine Learning Models for Audio Applications
  2. 3.2 Deep Learning & Advanced AI Techniques for Audio
  3. 3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
  4. 3.4 Transfer Learning in Audio AI
  5. 3.5 Use Case: Speech-to-Text Transcription for Medical Records
  6. 3.6 Case Study: AI-powered Music Generation with Deep Learning
  7. 3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow
4

Module 4: Speech Recognition & Text-to-Speech

  1. 4.1 Fundamentals of Speech Recognition & Phonetics
  2. 4.2 API-based ASR Solutions
  3. 4.3 Building Custom ASR Models with Transformers
  4. 4.4 Introduction to TTS & Voice Cloning
  5. 4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
  6. 4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
  7. 4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text
5

Module 5: Audio Enhancement & Noise Reduction

  1. 5.1 Common Audio Issues
  2. 5.2 AI-based Noise Filtering & Enhancement
  3. 5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
  4. 5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
  5. 5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
6

Module 6: Emotion & Sentiment Detection from Audio

  1. 6.1 Introduction to Emotion Detection
  2. 6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
  3. 6.3 Challenges: Bias, Multilingual Contexts, Reliability
  4. 6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
  5. 6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
  6. 6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples
7

Module 7: Ethical and Privacy Considerations

  1. 7.1 Deepfakes and Voice Cloning Risks
  2. 7.2 Privacy and Data Security
  3. 7.3 Bias and Fairness in Audio AI
  4. 7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
  5. 7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
  6. 7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist
8

Module 8: Advanced Applications & Future Trends

  1. 8.1 Sound Event Detection & Classification
  2. 8.2 Audio Search and Indexing
  3. 8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
  4. 8.4 Emerging Careers in Audio AI

AI Tools You’ll Learn

TensorFlow Audio Recognition

TensorFlow Audio Recognition

PyTorch Sound Classification

PyTorch Sound Classification

Librosa

Librosa

OpenAI Jukebox

OpenAI Jukebox

Google Magenta Studio

Google Magenta Studio

Audacity AI Plugins

Audacity AI Plugins

Adobe Podcast AI Tools

Adobe Podcast AI Tools

AIVA

AIVA

Wav2Vec

Wav2Vec

SpeechBrain

SpeechBrain

JUCE Framework

JUCE Framework

FL Studio with AI Integrations

FL Studio with AI Integrations

Logic Pro Smart Tools

Logic Pro Smart Tools

Sonible Smart EQ

Sonible Smart EQ

Spotify Audio Analysis API

Spotify Audio Analysis API

NVIDIA Riva Speech SDK

NVIDIA Riva Speech SDK

Deep Learning for Audio Toolkit

Deep Learning for Audio Toolkit

AudioLDM

AudioLDM

Sound Design Automation Tools

Sound Design Automation Tools

What Will I Learn?

  • Exam Format
  • 50 questions, 70% passing, 90 minutes, online proctored exam

About the instructor

M

Media

0 (0 ratings)

70 Courses

8 students

AIC_AI-Audio
$99.00

Material Includes

  • Included
  • Included Instructor-led OR Self-paced course + Official exam + Digital badge

Requirements

  • Requires basic programming knowledge in Python, familiarity with audio signal processing and machine learning concepts, comfort with linear algebra and probability, and hands-on experience using DAWs or audio software. A creative and experimental mindset is essential.

Target Audience

  • Duration
  • Instructor-Led: 1 day (live or virtual)
  • Self-Paced: 8 hours of content
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