Real-Time Audio DSP · Acoustics · Embedded Systems/Firmware
_{Seven years shipping audio firmware on constrained hardware — now working on the research questions underneath it.}

Adaptive feedback and howling suppression in closed acoustic loops — detection, notch allocation, frequency shifting, and how much stable gain you can actually buy. Sound-field reconstruction with calibrated uncertainty — when a spatial audio system says "the field here is X", what guarantee comes with that, and when should it refuse to answer? Real-time DSP on constrained hardware — what survives the trip from a MATLAB prototype to a DSP core with a fixed cycle budget, and what quietly doesn't. Physical and perceptual modelling for spatial and immersive audio.

The through-line: I've spent my career on the side of audio where the algorithm has to run — in a car cabin, a hospital corridor, a smart ring. I'm now interested in the part where it also has to be provably right.

Both are self-contained, documented, and reproducible from a clean checkout.

An open-source FAUST implementation of in-car communication (ICC): cabin speech picked up, processed, and replayed through the same cabin's loudspeakers — a closed electro-acoustic loop that wants to howl.

• Filterbank howling detector rather than FFT — FAUST expands FFT into scalar butterflies, so the filterbank is the structure that actually stays real-time
• Self-allocating notch cascade — slots claimed and released as howling frequencies come and go
• Frequency shifting via fi.pospass for additional loop-gain margin
• Measured, not just compiled: +4 dB MSG bypassed vs +16 dB suppressed — 12 dB of added stable gain on the reference synthetic cabin at 16 kHz, recorded in docs/tuning.md as a regression check
• Two bugs surfaced only by running it: an unnormalised cabin path with +9 dB peak gain (loop-gain readings were physically meaningless until fixed) and unstable loops reaching NaN, now soft-saturated

lib/icc.lib + three designs · ~700 lines of FAUST · verified against FAUST 2.70.3 · C/C++/Rust backends confirmed · CI · CITATION.cff

Conformal prediction gives distribution-free coverage, but under covariate shift the guarantee needs the likelihood ratio dQ/dP. Weighted conformal prediction (Tibshirani et al., 2019) is exact when that ratio is known — in most applications it has to be estimated, and estimation error is where coverage quietly goes.

In sound-field reconstruction it doesn't have to be estimated. The covariate is spatial position, and both the calibration-microphone density and the query/listener density are chosen by the experimenter — so dQ/dP is available in closed form from geometry, with no density-ratio estimation step. Known-ratio settings aren't unique to acoustics (randomised designs and importance sampling have them too).

Two things follow from having the ratio exactly rather than approximately. Where dQ/dP is genuinely unbounded — query points outside the calibration support — the method abstains with infinite intervals instead of silently under-covering; an estimated ratio cannot separate a true singularity from estimator blow-up. And because effective sample size is maximised when p = q, the optimal calibration-microphone layout is a sample from the query density — a placement result, not just an inference one.

Pure MATLAB, base install only — no toolboxes, fully synthetic image-source data, zero hardware.

Full-duplex dsp chain design for 1mic voice communication studies and implementation on AudioWeaver

Seven years of production audio and systems firmware — the reason I care about cycle budgets and failure modes rather than just algorithms.

Also: Visiting Researcher at IISc Bangalore (CSA) — sliding-mode control with control barrier functions for multi-agent robots, and over-synchronization in CUDA programs. Full-time open-source contributor to coreboot and the OpenID Foundation.

• IEEE 2026 — Author-oriented semantic plagiarism detection using transformer architectures
• IEEE 2026 — Transformer-based authorship attribution: fine-tuning BERT and S-BERT for stylometric analysis
• arXiv 2025 — Conversational AI dialog for medicare, powered by fine-tuning and retrieval-augmented generation
• 2024 — AI-engine-based acceleration for high-performance programmable SoC designs
• 3 published Indian patents (Dec 2024) — semantic role labelling for sentiment analysis · RAG-based conversational AI · fine-tuned BERT for relation extraction and NER
• In preparation — SoundFieldUQ; neuromorphic drowsiness prediction with multi-sensor fusion

🔗 ORCID 0009-0001-5506-9588

Audio & DSP — FAUST · MATLAB · Audio Weaver · Qualcomm AudioReach · Android Audio Framework · ALSA · Kalimba DSP · IIR/FIR design, AEC, AGC, noise suppression

Systems — C · C++ · Python · Zephyr · FreeRTOS · ThreadX · Linux kernel · ARM Cortex-M3/M7/A53 · x86 · BLE, I2S, I2C, UART · JTAG/J-Link/Trace32 · Git, Gerrit, CI

Graduate in keyboard from Bangiya Sangeet Parishad, and in painting from Tripura Fine Arts Academy with distinction. Dhwaani — my handle — means resonance.

Happy to talk about adaptive feedback control, spatial audio, uncertainty quantification, or getting DSP to survive on a small core.