About Me

sample-image Hi! I am Neeraj Sharma, a Research Scholar, pursuing my PhD in ECE at Indian Institute of Science (IISc), Bangalore. I will moving on to take up the BrainHub CMU-IISc Fellowship after submission of my PhD thesis. I joined Indian Institute of Science (IISc) in 2009 after completing a B.Tech in Instrumentation and Electronics Engineering from College of Engineering and Technology, Bhubaneswar. My interests are in signal sampling, and reconstruction, transform-domain analysis, and pattern recognition. My research is focussed on obtaining an information reconstruction methodology with a flavour motivated from signal to neural transduction mechanism in mammalian hearing. Focus is on 1-D signals, and more specifically audio signals. The goal here is not perfect signal reconstruction but perfect information reconstruction. This brings the interesting issue of sampling the information of interest in the signal rather than sampling the whole signal. We (I with Prof. TV Sreenivas, my advisor) think this is how it may be happening when we hear!
EMAIL-ID:
X@Y.com where, X is neerajww and Y is gmail


Updates

  • [01-02-2017] I am awarded with the BrainHub Carnegie Mellon University-IISc Fellowship. The fellowship will provide me with funding support for 12 months to explore the perceptual space associated with ''speech listening in the wild''. Together with Sriram Ganapathy (LEAP Lab, IISc) and Lori Holt (Speech Perception and Learning Lab, CMU) we will work on understanding human audition associated with listening in overlapped speech scenarious, and integration of the findings to automatic speech recognition system design.
  • [16-09-2016] I will be graduating soon!
    Click the image below to see an illustration of thesis overview.
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    Meanwhile, I am in search for my next research "habitat".
    In case you have any opportunities (Post-Doc/Internship/Research Scientist positions), I will be happy to know more.
  • [15-07-2016] Gave my PhD Colloquium presenting our research findings.
  • [(04-08)-07-2016] Participated in the enthralling Summer School on Speech source modelling and its applications, held at Gandhinagar.

Ongoing Doctoral Thesis

sample-image Auditory motivated signal sampling and representation techniques: Application to efficient analog-to-information conversion Sound is a stimuli which has been studied scientifically and modeled mathematically. The efforts have been quite successful when we look at the way our voice is transmitted for communication or music processing is done in studios based on these principles. However, the many tasks such as speech recognition, language learning, sound mixture segregation remain open problems for machine implementation and also speech processing through cochlear implants is far from satisfactory. We attempt to learn from the way mammalian auditory system performs signal processing and pattern recognition. The biological signal processing aspects are nonlinear and do not in general fall into the paradigm of linear time-invariant systems analysis. The performance (of the task performed) shows a graceful degradation with noise. With this motivation we are focusing on issue of efficient sampling, reconstruction, representation and coding of audio signals. In this research, we are exploring the following sub-topics which could yield new techniques for information and signal reconstruction with focus on auditory processing like representations. A. sparse representation (for example compressive sampling) based reconstruction, B. random sub-Nyquist, event-triggered sampling C. noise assisted signal processing (example using stochastic resonance)

A Doctoral Thesis ... a body of research that, in a small way, will move a field forward.

Courses Taken

Random Processes, Pattern Recognition and Neural Networks, Time-Frequency Analysis, Adaptive Signal Pro- cessing, Matrix Theory, Digital Signal Compression, Non-linear Signal Processing, Stochastic Models for Speech Recognition, Digital Image Processing, Introduction to Neuroscience. The above courses are the few I took from the big list at IISc.

Teaching Assistantship

Time Frequency Analysis (E9-213) in Jan-2012. Course was offered by Prof. Chandra Sekhar Seelamantula. Signal Quantization and Compression (E9-221) in Aug-2011. Course was offered by Prof. T. V. Sreenivas.

Internships, Conferences, and Workshops

Audition Lab, Ecole Normale Superiere (ENS), Paris, [14 Apr to 08 June, 2014]
I was a Visiting Student at Audition Lab. I worked on an interesting concept of designing auditory skectches. I worked with mentorship and support from Daniel Pressnitzer, and Laurent Daudet who had initially proposed the concept. I carried the concept further, and designed auditory sketches of a sound by jointly using peaks in time-frequency and rate-scale-time-frequency planes. I also proposed a metric to quantify the notion of auditory sketches suitable to compare quantify two sketches. The work is in progress and likely we will summarize it someday soon.
Winter School in Speech and Audio Processing (WiSSAP), 2010-15, India
I have attended all the WiSSAPs in this time span. It is a yearly workshop, and a very good learning experience to broaden what I do not know, and should know! In WiSSAP-2015 I gave a talk on Auditory modeling in the workshop. All the talks are hosted here: click.
Mechanics of Hearing (MoH), 2014, Athens
This is one of the best workshop I have attended. With all excellent researchers in one hall, and examining each others insights, it was amazing. I had a poster here. Got in touch with wonderfull people in auditory modeling.
Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2012, Kyoto
My first outside India conference visit. Again very nice experience.
Int. Conf. Signal Processing and Communication (SPCOM), 2010,-12,-14, IISc Bangalore
My first conference presentation in IISc. Our paper was selected in the top papers in the conference.
Student Chapter Leadership Workshop at Photonics Europe, 2014, Brussels
What makes a leader a leader! Spent 6 hrs with close to 20 people meeting first time, and each from a different country. Each sounded his/her thoughts on variety of topics and case-studies. Realized - Once you talk, it is then very easy to talk :-).
Workshops on Signal Processing, Machine Learning in IISc, 2009-14
Learnt lot with a trade-off of time consumption.

Peer-reviewed Published Findings

Sparse signal reconstruction based on signal dependent non-uniform samples (In ICASSP'12, Kyoto)

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The classical approach to A/D conversion has been uniform sampling and we get perfect reconstruction for bandlimited signals by satisfying the Nyquist Sampling Theorem. We propose a non-uniform sampling scheme based on level crossing (LC) time information. We show stable reconstruction of bandpass signals with correct scale factor and hence a unique reconstruction from only the non-uniform time information. For reconstruction from the level crossings we make use of the sparse reconstruction based optimization by constraining the bandpass signal to be sparse in its frequency content. While overdetermined system of equations is resorted to in the literature we use an undetermined approach along with sparse reconstruction formulation. We could get a reconstruction SNR >20dB and perfect support recovery with probability close to 1, in noise-less case and with lower probability in the noisy case. Random picking of LC from different levels over the same limited signal duration and for the same length of information, is seen to be advantageous for reconstruction.

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Event-triggered sampling and reconstruction of sparse trigonometric polynomials (In SPCOM'14, Bangalore)

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We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

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Moving Sound Source Parameter Estimation Using A Single Microphone And Signal Extrema Samples (In ICASSP'15, Brisbane)

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Estimating the parameters of moving sound sources using only the source signal is of interest in low-power, and contact-less source monitoring applications, such as, industrial robotics and bio-acoustics. The received signal embeds the motion attributes of the source via Doppler effect. In this paper, we analyze the Doppler effect on mixture of time-varying sinusoids. Focusing, on the instantaneous frequency (IF) of the received signal, we show that the IF profile composed of IF and its first two derivatives can be used to obtain source motion parameters. This requires a smooth estimate of IF profile. However, the numerical implementation of traditional approaches, such as analytic signal and energy separation approach, gives oscillatory behavior hence a non-smooth IF estimate. We devise an algorithm using non-uniformly spaced signal extrema samples of the received signal for smooth IF profile estimation. Using the smooth IF profiles for a source moving on a linear trajectory with constant velocity, an accurate estimate of moving source parameters is obtained. We see promise of this approach for an arbitrary trajectory motion parameter estimation.

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Time-instant Sampling Based Encoding of Time-varying Acoustic Spectrum (In MoH'14, Athens)

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The inner ear has been shown to characterize an acoustic stimuli by transducing fluid motion in the inner ear to mechanical bending of stereocilia on the inner hair cells (IHCs). The excitation motion/energy transferred to an IHC is dependent on the frequency spectrum of the acoustic stimuli, and the spatial location of the IHC along the length of the basilar membrane (BM). Subsequently, the afferent auditory nerve fiber (ANF) bundle samples the encoded waveform in the IHCs by synapsing with them. In this work we focus on sampling of information by afferent ANFs from the IHCs, and show computationally that sampling at specific time instants is sufficient for decoding of time-varying acoustic spectrum embedded in the acoustic stimuli. The approach is based on sampling the signal at its zero-crossings and higher-order derivative zero-crossings. We show results of the approach on time-varying acoustic spectrum estimation from cricket call signal recording. The framework gives a time-domain and non-spatial processing perspective to auditory signal processing. The approach works on the full band signal, and is devoid of modeling any bandpass filtering mimicking the BM action. Instead, we motivate the approach from the perspective of event-triggered sampling by afferent ANFs on the stimuli encoded in the IHCs. Though the approach gives acoustic spectrum estimation but it is shallow on its complete understanding for plausible bio-mechanical replication with current mammalian auditory mechanics insights.

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Event-triggered Sampling Using Signal Extrema for Instantaneous Amplitude and Instantaneous Frequency Estimation (In Signal Processing'15, Elsevier)

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Event-triggered sampling (ETS) is a new approach towards efficient signal analysis. The goal of ETS need not be only signal reconstruction, but also direct estimation of desired information in the signal by skillful design of event. We show a promise of ETS approach towards better analysis of oscillatory non-stationary signals modeled by a time-varying sinusoid, when compared to existing uniform Nyquist-rate sampling based signal processing. We examine samples drawn using ETS, with events as zero-crossing (ZC), level- crossing (LC), and extrema, for additive in-band noise and jitter in detection instant. We find that extrema samples are robust, and also facilitate instantaneous amplitude (IA), and instantaneous frequency (IF) estimation in a time-varying sinusoid. The estimation is proposed solely using extrema samples, and a local polynomial regression based least-squares fitting approach. The proposed approach shows improvement, for noisy signals, over widely used analytic signal, energy separation, and ZC based approaches (which are based on uniform Nyquist-rate sampling based data-acquisition and processing). Further, extrema based ETS in general gives a sub-sampled representation (relative to Nyquist-rate) of a time-varying sinusoid. For the same data-set size captured with extrema based ETS, and uniform sampling, the former gives much better IA and IF estimation.

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Other than Research

a) Execom Member of IEEE-IISc Student Branch (2012-13)
Got Best Volunter Award for the year 2012-13. Together with a very sporty team of volunteers in our Execom, and team lead by Prof. T. Srinivas we had a wonderful set of activities in and around campus.
b) IISc ECE Dept. WebTeam Member (2014-15)
Together with set of 3 more members and spearheaded by Prof. Chandra R. Murthy I often maintain the ECE website.
c) Sunday Cricket League (SCL, 2014-15)
Every sunday we have huge fun making our adrenaline flow to bowl, bat and field.
d) Camera clicks
Very often I get amazed by nature, and on getting an opportunity I click-capture-upload some pictures here: click to see. What you see only deciphers your thoughts.:-)
Also, I like the amazing natural beauty in IISc campus. My collection of some photography in the campus is here: click to see. I find it very difficult to prune the selection!

Quotes

Use the sunrise as an alarm. It has no snooze.

Thoughts

Good and bad is a function of surroundings.

Haricharan

Write-ups, Talks, Good books, and ...

"Throwing Light into the Tunnel: auditory models and perception"
[inivited talk in WiSSAP-2015, 04-01-2015] Click here to get the PDF.

"Sound Analysis: some knowns and unknowns"
[in SIAM-IISc Chapter Student Talk Series, @IISc, 08-05-2015]
Click here to get the PDF.

"Detect and Sample: an event-triggered approach for data acquisition and processing"
[Work Discussion at ICTS-IISc Workshop, 08-01-2015]

"Turns are Good: Processing Extrema of a Nonstationary Narrowband Signal"
[Delivered in Spectrum Lab, IISc, 22-10-2013]

"Function Approximations"
[Links to some good PDFs, 11-01-2016] Taylor, Fourier, Chebyshev, Pade, ... Click here to get the PDF.

"Detect and Sample: Questioning uniform Nyquist-rate sampling"
[Delivered on IEEE Day celebrations in campus, 01-10-2013]

Technical books I have liked: I sometimes update the rarely updated list here: click .