This document is a resume for Abhijith Mundanad Narayanan. It outlines his education, including a Master's degree in Signal Processing from Indian Institute of Science and a Bachelor's degree in Electronics and Communication from University of Kerala. It also details his programming skills, software familiarity, work experience including projects at Qualcomm Technologies related to DSL technology implementation, and academic projects related to music signal processing. Additionally, it provides his contact information and highlights achievements like GATE exam scores and publications.

1. Abhijith Mundanad Narayanan
New #12, Sai Nilayam, New Thippasandra, Bangalore-75 | +918861551998 | mabhijithn@gmail.com
Education
MASTER OF ENGINEERING | 2012-2014 | INDIAN INSTITUTE OF SCIENCE (6.1/8.0 – CGPA)
 Major: Signal Processing
Related coursework: Probability, random processes, detection and estimation
BACHELOR OF TECHNOLOGY | 2005-2009 | UNIVERSITY OF KERALA (7.57/10.0 – GPA)
 Major: Electronicsand Communication
Skills & Abilities
PROGRAMMING SKILLS
C, C++, Python,shell scripting.
SOFTWARE PACKAGES/OS FAMILIAR WITH
MATALB, Octave,OpenWRT, Buildroot
Experience
ENGINEER | QUALCOMM TECHNOLOGIES, BANGALORE | JULY, 2014 TO PRESENT (2 YEARS)
A member of DSP Firmware group involved in implementation of algorithms in physical layer for
DSL technology.
Project:GIGADSL (1GBPS OVER COPPER) CO AND CPE FIRMWARE – FALL-BACK SUPPORT AND BRINGUP
Description:GigaDSLtechnology is a Qualcomm proprietary technology delivering speeds upto
1Gbps overcopper.
Responsibilities:Developmentand implementation of algorithms forDSL physical layer functions.
Deliverables:
 Implemented seamless rate adaptation (SRA) feature on GigaDSL forsupporting fall-backto
30MHz profile.
 Contributed towards C based frameworkformicrocontroller code.Migration for assembly
code to C-based frameworkfor ease of development and debugging. Implemented seamless
rate adaptation support on the C framework.
 Supporting bring up of firmware on GigaDSL chip by fixing bugs.
 Skillset: C, shell and TTLscripting
Project:SOFTWARE DEVELOPMENT FOR IKANOS RESIDENTIAL GATEWAYS
Description:Developmentof softwareto be run on residential gateway products of Ikanos
communication.

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Responsibilities:Developmentand implementation of DSP algorithms related to VoIP on
residential gateways.Contributing towards producing shippable code periodically.
Deliverables:
 Contributed in increasing the tail length of echo cancellerto 64ms from 32ms.
 Involvedin fixing of various bugs to enable shippable codein 2 week Agile based
development cycle.
 Skillset: C, shell scripting, basic Linux kernel programming OpenWRTand buildroot.
LECTURER | IES COLLEGE OF ENGINEERING, THRISSUR, KERALA | DECEMBER, 2009 TO MAY, 2011 (1.5
YEARS)
Delivered digital signal processing course to undergraduate students.
Achievements
Secured ‘S’(top)gradeforthe project in ME (Signal Processing) fromDepartment of Electrical
Communication Engineering at Indian Institute of Science (IISc).
Secured All-India-Rankof75(99.96percentile)inGATE 2012(Electronicsand Communication
Engineering)
Secured Kerala state rank of 702 (99.97percentile) in Common Entrance Examination for
Engineering in 2005.
Publications
Abhijith,M. N., Prasanta K. Ghosh, and K. Rajgopal."Multi-pitchtrackingusing Gaussianmixture
model with time varying parameters and Grating Compression Transform."Acoustics, Speech
and Signal Processing (ICASSP), 2014 IEEE International Conference on.IEEE, 2014.
Academic Projects
LEAD INSTRUMENT SEPARATION FROM POLYPHONIC MUSIC USING PRIOR PITCH INFORMATION
A melody contour forpolyphonic music is obtained. The melody contouris used as prior
information for a simple spectrogram reconstruction. Separation of lead instrument is carried out
by synthesis fromspectrogram.
ESTIMATION OF PITCH USING SPECTROGRAM DEMODULATION
The pitch of a speaker is estimated using Grating Compression Transform. The workis extended to
multi-pitch estimation using a Time-Varying GMM framework.
MUSICAL INSTRUMENTS (FROM SINGLE NOTES) CLASSIFIER ON BEAGLEBOARD USING MFCC
MFCC is extracted fromindividual notes of instruments. Clustering is used to classify notes of
unknown instrument to one of the instruments in the set. The project was implemented on a single-
board computer, viz.,BeagleBoard.