Categories AI + Business, Media

AI+ Audio™

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

$82.00

Intermediate
AI+ Audio.
24h
1 Year.
$82.00
Training 5 or more people?

Next-level music and sound design.

  • 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
  • Revolutionizes Sound Creation Learn how AI automates composition, mixing, and mastering, making audio production faster and more innovative.
  • Enhances Audio Quality Use AI tools to clean, balance, and optimize sound for professional-grade results across platforms.
  • Personalizes Listening Experiences Discover how AI tailors music and soundscapes to individual preferences in real time.
  • Bridges Creativity and Technology Combine artistic vision with AI-driven tools to create immersive, next-generation audio experiences.
  • Expands Career Opportunities Gain industry-ready skills for roles in music tech, sound design, gaming, and multimedia production.

Course Curriculum

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

Learning Objectives

  • Learn AI Applications in Audio: Grasp how AI enhances technologies like speech recognition, emotion detection, and audio enhancement.
  • Develop Practical AI Skills: Apply AI-driven tools and APIs for real-world applications such as transcription and noise reduction.
  • Equip with Ethical Knowledge: Understand ethical considerations, data privacy, and bias mitigation in AI-driven audio technologies.
  • Grasp Real-World Audio AI Challenges: Navigate challenges in the AI + audio space, preparing for innovation and leadership roles.
  • Demonstrate AI + Audio Expertise: Be well-equipped to lead and innovate in industries combining AI and audio technologies.

Resource Center

  • Videos, Use cases, Workshops, E-books and tools.

Target Audience

  • Aspiring Audio Engineers.
  • Music Producers and Composers.
  • Machine Learning Enthusiasts.
  • Game and Media Developers.
  • Tech Innovators and Researchers.

Job Roles & Industry Outlook 

Al Sound Designer
Design Al-driven soundscapes, automate mixing and mastering processes, and generate adaptive audio for games, films, and virtual environments.
Audio Technology Manager
Lead the integration of Al tools in music production, post-processing, and sound engineering to streamline workflows and boost creative output.
Chief Audio Innovation Officer (CAIO)
Drive Al transformation in the audio industry by championing intelligent sound design, personalized listening technologies, and next-generation auditory innovation.
Al Audio Engineer
Develop intelligent sound systems that adapt to user environments, enhance audio quality, and create dynamic, immersive listening experiences across platforms.
Audio Data Scientist
Analyze sound data to build predictive models for music recommendation, voice recognition, and personalized audio experiences.

Exam Information

Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Fees include
One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Introduction to AI and Sound – 7%
Harnessing AI Across Audio Domains – 15%
Machine Learning & AI for Audio – 15%
Speech Recognition & Text-to-Speech – 15%
Audio Enhancement & Noise Reduction – 12%
Emotion & Sentiment Detection from Audio – 12%
Ethical and Privacy Considerations – 12%
Advanced Applications and Future Trends – 12%

Explore our Schedules

Nov 15 - Nov 17
GMT 09:00 AM - 05:00 PM
In-person
Doha
724.5$
3 Days
Sep 20 - Sep 22
GMT 09:00 AM - 05:00 PM
In-person
Muscat
724.5$
3 Days

Certificate of Completion

AI+ Audio.

$82.00

Intermediate
AI+ Audio.
24h
1 Year.
$82.00
Training 5 or more people?

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