AI+ Audio Practitioner™

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

₹8,999

Delivered by UpskillNexus, an AI CERTs® Authorized Training Partner.

About this certification

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

What's included

Package

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

Duration

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

Exam format

50 questions, 70% passing, 90 minutes, online proctored exam

Prerequisites

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.

Course modules

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

Why this certification matters

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.

Who should enrol

Aspiring Audio Engineers – Ideal for those looking to integrate AI into sound design, mixing, and mastering.

Music Producers and Composers – Perfect for creators who want to use AI tools for music generation and adaptive composition.

Machine Learning Enthusiasts – Great for learners eager to apply ML models to audio analysis and synthesis.

Game and Media Developers – Suitable for professionals aiming to create intelligent, immersive, and responsive sound environments.

Tech Innovators and Researchers – Designed for individuals exploring cutting-edge AI applications in audio technology and digital sound innovation.