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.
DRDO · Smart Vehicles · Hardware
Free data, numeric targets and a demo the judge hears rather than reads make this one of the most winnable statements here — just remember it is a Hardware entry, so get the quantised model running live on a board with real microphones early and report impulsive noise separately.
What it actually is
Radio communication in combat is drowned out by gunfire, artillery, rotor noise and vehicle engines, and the classic filtering methods fail because they assume the noise stays roughly the same, which gunfire emphatically does not. The ask is a neural noise suppressor that strips those sounds out while leaving the speech clear and understandable. It has to run live on a small embedded board, not a workstation.
What to build
A speech enhancement system trained on clean speech mixed with the defence noise classes the statement names — gunshots, artillery, rotor noise, vehicle engines, sirens and wind — across a range of signal-to-noise ratios so both stationary and impulsive cases are covered, using a model that operates in the complex domain to preserve phase and combines full-band and sub-band features, trained against SI-SNR and perceptual losses and evaluated on the three metrics the statement targets numerically, then quantised and converted for real-time edge inference with an optional adaptive filter mopping up residual noise, and finally integrated with a primary and reference microphone pair and a headset so the enhancement runs live end to end.
Smallest thing that wins the room
Put the headset on a judge, play a recording of speech under helicopter rotor noise with a gunshot in the middle, and let them hear the raw and enhanced channels switch back and forth with the live latency displayed.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 96% of the 226 · #11 of 226 by expected field
Few teams are likely to go here. The best odds on the board come from statements like this.
Why: defence, intelligence and space bodies drew small fields; hardware halves the field a software statement gets.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
4/5Numeric targets mean success is defined rather than argued, the training data is entirely free, the demo works on a judge's own ears, and defence audio is a far thinner field than the general AI themes — the main caution is that the impulsive case is genuinely much harder than the stationary one and the targets are quoted as if they were uniform.
Feasibility
4/5This is one of the best-supported statements on the portal — clean speech corpora, noise banks containing gunshots, helicopters and engines, and speech enhancement benchmark sets are all freely available, the model architectures and the three named metrics are standard open implementations, and useful edge inference runs on boards far cheaper than the development kit the statement mentions.
Innovation scope
3/5The statement prescribes the pipeline in detail down to the transform, the complex-domain operation, the loss functions, the augmentation strategy and the optimisation path, so your real design freedom is the architecture and how you handle impulsive events, which behave nothing like the stationary noise the rest of the pipeline assumes.
Clarity
5/5Exemplary — it names the noise classes, the feature representations, the loss functions, the evaluation metrics with numeric targets of SNR above 15 decibels, STOI above 0.85 and PESQ above 2.5, the deployment hardware, the optimisation techniques and the microphone integration.
Effort
HeavyA dataset mixing pipeline, model training to stated targets, quantisation and runtime conversion, real-time streaming inference with bounded latency, and physical integration with microphones and a headset is five workstreams, and the embedded real-time path is where teams that trained a good model discover they cannot ship it.
Demo-ability
EasyThis is an auditory demo and those are rare and visceral — a judge hears the difference in their own ears rather than reading a metric, and switching live between the raw and enhanced channels is more convincing than any chart of PESQ scores.
In its favour
- Green flag: The three performance targets are numeric, so you can prove you succeeded instead of claiming it — this is unusual and it makes the whole submission objectively assessable
- Green flag: Clean speech corpora and environmental noise banks containing gunshots, helicopters and engine sounds are all free and abundant, so the dataset pipeline the statement asks for is genuinely straightforward to build
- Green flag: An auditory demo lands harder than a visual one and almost nothing else at the event will have one — the judge experiences the result rather than evaluating a claim about it
- Green flag: You do not need the expensive development kit named in the statement; a far cheaper edge board running a quantised model demonstrates the same real-time claim and shows better engineering judgement
Against it
- Red flag: Impulsive noise is a fundamentally different problem from stationary noise — a gunshot is a broadband transient that masks speech completely for its duration, and a model that hits the targets on rotor noise will fall well short on gunfire unless you train and report for it separately
- Red flag: The category is Hardware, so a notebook demonstration with excellent metrics does not answer the statement — the embedded real-time path with microphones and a headset is a requirement, not a stretch goal
- Red flag: Training on synthetically mixed noisy-clean pairs generalises poorly to real recordings where the noise is acoustically coupled and reverberant, so include real noisy recordings in evaluation even though the statement only asks for synthetic generation
- Red flag: Real-time means bounded end-to-end latency including buffering, and a model with excellent PESQ at 200 milliseconds of algorithmic delay is unusable in a duplex radio conversation
What you will be writing
- DCCRN or Conv-TasNet complex-domain enhancement
- SI-SNR and perceptual loss training objectives
- PESQ / STOI / SNR evaluation harness
- ONNX and TensorRT quantised edge deployment
- dual-microphone primary and reference capture
- LMS adaptive filter for residual suppression
- Speech enhancement
- Edge AI deployment
- Defence communications
Prior art to read before you start
neural noise suppression for speech · real-time embedded inference optimisation · impulsive noise robustness
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.