Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Sound Modification explores how machine learning reshapes what we can do with audio. You learn to apply AI to core sound tasks: enhancing and denoising audio, separating sources (isolating vocals or instruments), transforming timbre and voice, and generating new sound. The course covers the audio representations and neural methods behind these tools and connects them to real uses in music production, speech and sound design. You finish able to reason about applying AI to a sound-modification problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in sound modification — using machine learning to transform, enhance, separate and generate audio for music, speech and sound design.
📋 Course Objectives
1. Represent audio for machine learning.
2. Enhance and denoise audio.
3. Separate sources from a mix.
4. Transform timbre and voice.
5. Apply generative methods to sound.
👥 Who Should Enroll?
• Audio engineers and producers
• Sound designers and musicians
• DSP and ML developers
• Students of audio technology
🚀 Key Learning Outcomes
• An understanding of AI in audio.
• A sound-transformation perspective.
• An audio-AI project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Signal Fundamentals
• Sampling, quantisation, aliasing and the constraints they impose
• Time-frequency representation: STFT, mel spectrograms and phase
• Loudness, dynamic range and perceptual measures that matter to listeners
Isolating and Recombining Sound
• Music and speech source separation architectures
• Dereverberation, denoising and artefact trade-offs
• Evaluation with SDR and perceptual listening tests, not loss curves
Voice and Timbre Conversion
• Pitch and time manipulation without formant distortion
• Voice conversion and timbre transfer approaches
• Neural vocoders and the quality-versus-latency trade-off
Synthesis and Text-to-Audio
• Text-to-speech and controllable prosody
• Generative audio and music models, and their controllability limits
• Real-time constraints for live and interactive use
Consent, Attribution and Detection
• Voice cloning consent, likeness rights and disclosure obligations
• Training-data provenance and rights in music and speech corpora
• Watermarking and synthetic audio detection, and their fragility
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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