About Aryamaan Dash

Current Focus

• Software Engineering

  Responsive React.js, HTML, and CSS interfaces, with experience contributing as both a front-end developer and team lead.

• Embedded Systems

  Real-time C++ DSP, Verilog, ESP32 development, Daisy Seed, and soldered prototypes.

• Machine Learning

  Audio feature extraction, classifier evaluation, PyTorch, TensorFlow, and scikit-learn.

• Embedded audio

  Programmable Multi-Effects Guitar Pedal

  Built a Daisy Seed-based programmable guitar pedal with selectable bypass, distortion, reverb, delay, and flanger effects. Programmed real-time embedded C++ DSP firmware using DaisySP, audio callbacks, delay-line processing, wet/dry mixing, and debounced mode switching. Soldered a perfboard prototype integrating guitar input buffering, audio jack wiring, and control inputs.

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• Machine learning

  Guitar Audio Classification Model

  Built an audio classification pipeline with 89% test accuracy to identify four guitar sound categories: single notes, chords, palm-muted playing, and background noise from self-recorded audio. Processed one-second WAV clips and extracted MFCCs, RMS energy, spectral centroid, bandwidth, rolloff, and zero-crossing-rate features. Trained and evaluated a logistic regression classifier using multi-session training, validation, and test splits on independently recorded guitar samples.

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• Study tool

  Study Tracker

  Developed a full-stack study tracking web application using Next.js, TypeScript, Prisma Postgres, and Vercel for logging, managing, and reviewing study sessions. Built Chart.js visualizations for study trends, session history, and progress insights, plus persistent Prisma Postgres storage for reliable user data retrieval.

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Tools I Use

• C++
• Python
• TypeScript
• HTML/CSS
• Verilog
• R
• Git
• Vivado
• Next.js
• ESP32
• Daisy Seed
• Soldering
• PyTorch
• TensorFlow
• scikit-learn