To develop an AI/ML-enabled adaptive noise cancellation (ANC) system that effectively suppresses stationary, non-stationary, and impulsive defence noises while maintaining high speech intelligibility and real-time performance on embedded hardware.
“In high-noise military environments, soldiers and operators struggle to communicate clearly because loud noises like gunshots, helicopter blades, and engine roars overpower human speech. We need to build an intelligent hardware and software system that instantly cleans up this chaotic background sound while keeping the speaker's voice completely clear and natural. This system must run locally on small embedded devices in real-time without internet access.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An AI/ML-powered edge hardware prototype combining dual-microphone input, a deep learning speech enhancement model running on a compact processing board (like NVIDIA Jetson or Raspberry Pi with AI accelerators), and a low-latency output headset.
WHO IS IT FOR?
Primary beneficiaries are defense personnel, army soldiers, tank commanders, and aerospace pilots. The stakeholders are the Department of Defence Production, DRDO, and IDEX.
WHY DOES IT EXIST?
Traditional noise suppression filters are rigid, assume static background noise, and fail terribly when hit with sudden, explosive sounds like gunfire or heavy artillery, often clipping human voice or lagging behind, which can lead to mission-critical miscommunications.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Real-time dual-channel audio feeds from a primary microphone (capturing speech + noise) and a reference microphone (capturing ambient noise), along with pre-loaded defence noise training datasets.
Audio is converted into time-frequency spectrograms, processed via deep learning neural networks for complex-domain mask estimation and noise suppression, followed by optional adaptive filtering (LMS) for residual cleanup and hardware quantization/TensorRT optimization.
Clean, crystal-clear, real-time speech delivered directly to communication headsets with sub-15ms latency, achieving high intelligibility metrics.
CONSTRAINTS
- Must operate completely offline without cloud connectivity
- End-to-end processing latency must be strictly under 20 milliseconds for real-time military communication
- Must handle sudden high-decibel impulses (gunshots, explosions) without clipping or blowing out output speakers
- Must fit and execute smoothly on resource-constrained embedded edge hardware
JURY METRICS
- Signal-to-Noise Ratio (SNR) > 15 dB improvement
- Short-Time Objective Intelligibility (STOI) > 0.85
- Perceptual Evaluation of Speech Quality (PESQ) > 2.5
- Inference latency on target embedded hardware (<20ms)
CORE BOTTLENECK
Effectively suppressing abrupt, non-stationary impulsive sounds (like sudden gunshots) without distorting the speaker's voice or introducing unnatural musical noise artifacts, all while maintaining ultra-low latency on embedded hardware.
Why You Should Choose This PS (Pros)
- Extremely high national security and defense impact aligned with Atmanirbhar Bharat / IDEX initiatives
- Clear, quantifiable evaluation benchmarks (PESQ, STOI, SNR)
- Great visual and tactile 'wow factor' during offline evaluation with a live working hardware prototype
Challenges & Risks to Watch (Cons)
- Requires access to specialized hardware (Jetson Nano/Orin, DSP kits, audio codecs)
- Curating or synthesizing realistic, high-fidelity defense noise datasets can be tedious
- Audio debugging and latency optimization at the driver/kernel level can be notoriously tricky under tight hackathon deadlines
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
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In defence and mission-critical communication systems, reliable speech transmission is severely affected by diverse acoustic disturbances such as gunshots, artillery fire, helicopter rotor noise, armored vehicle sound and emergency sirens. Traditional signal processing techniques—like spectral subtraction, Wiener filtering, and classical LMS-based ANC—are limited in handling highly dynamic and non-linear noise environments. These methods assume stationary noise characteristics and often introduce artifacts or speech distortion under rapidly changing conditions.
Recent advancements in Artificial Intelligence and Machine Learning (AI/ML)
have transformed the field of speech enhancement and ANC. Deep learning models and time-domain architectures are capable of learning complex spectral-temporal patterns directly from data. These models significantly outperform conventional approaches in terms of perceptual quality (PESQ), intelligibility (STOI), and noise suppression (SNR). Additionally, the rise of edge AI platforms enables deployment of such models on embedded systems for real-time applications.
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The proposed system integrates AI/ML-driven noise suppression with adaptive filtering to create a robust ANC pipeline. The development begins with dataset generation, where clean speech data is combined with curated defence noise datasets (gunshots, drones, artillery, vehicle engines, wind, etc.)
at varying SNR levels. This synthetic data generation ensures coverage of both stationary and impulsive noise scenarios.
The training pipeline involves transforming audio into time-frequency representations (e.g., STFT spectrograms) or directly using raw waveform inputs. Models process both full-band and sub-band features to capture global and local dependencies. while its also operates in the complex domain to preserve phase information. Training is performed using loss functions such as SI-SNR, L1/L2 loss, and perceptual loss, with evaluation metrics including SNR, STOI, and PESQ. Data augmentation techniques (random noise mixing, reverberation, clipping) are applied to improve generalization.
During inference, the trained model processes incoming noisy audio in real time, estimating a mask or directly reconstructing enhanced speech. The system can optionally include a lightweight adaptive filter (e.g., LMS) for residual noise suppression.
For prototype demonstration, the trained model is deployed on embedded/edge hardware such as DSPs or AI-enabled SoCs (e.g., NVIDIA Jetson AGX Orin 64GB Developer Kit or similar platforms). Optimization techniques like quantization, pruning, and ONNX / TensorRT conversion are applied to meet latency and power constraints. The system is integrated with microphones (primary + reference) and headphones/communication units to validate real-time ANC performance in practical environments
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The final solution is a hybrid AI-driven ANC system capable of operating in real-time and handling diverse noise environments, including impulsive and highly dynamic defence scenarios. It should include: