Publications
Accepted
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VaPar Synth - A Variational Parametric Model for Audio Synthesis
Krishna Subramani,
Preeti Rao
Alexandre D'Hooge
ICASSP 2020
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We present VaPar Synth - a Variational Parametric Synthesizer for instrument note synthesis, which utilizes a conditional variational autoencoder trained on a source-filter inspired parametric representation.
We will be presenting our work virtually at ICASSP 2020! (Presentation Video)
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Energy-Weighted Multi-Band Novelty Functions for Onset Detection in Piano Music
Krishna Subramani,
Srivatsan Sridhar,
Rohit M A,
Preeti Rao
National Conference on Communications 2018
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Propose the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold to improve detection of soft onsets in the vicinity of loud notes. Also propose a grouping algorithm to reduce the detection of spurious onsets.
We presented our work at the conference as an oral presentation
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Generative Audio Synthesis with a Parametric Model
Krishna Subramani,
Alexandre D'Hooge,
Preeti Rao
ISMIR 2019 Late Breaking/Demo
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Audio Examples /
BibTeX
Propose a parametric representation for audio corresponding more directly to its musical attributes such as pitch, dynamics and timbre. For more control over generation, we also propose the use of a conditional variational autoencoder which conditions the timbre on pitch.
We presented our work as a poster in the ISMIR 2019 Late Breaking/Demo session
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Submitted
Research Experience
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Variational Parametric Models for Audio Synthesis
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Review of Generative Models for Audio Synthesis
(Ongoing)My Master's thesis work on generative parametric models for audio synthesis.
I presented the work done by me so far as an oral presentation for the first stage defense of my Master's thesis
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Compression using Graph Signal Processing
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Notebook Demonstration of the Code
Inspired by the authors work on compressing distributed data by modeling it as graphs, we decided to try out and extend the idea to a new dataset by performing outlier removal, data imputation and clipping, and were able to successfully compress it at a significantly faster rate(~8x speedup) than conventional techniques.
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Data Sonification using Granular Synthesis
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Code Repository
Implemented a real-time granular synthesizer on Pure Data. Also ideated a scheme where the parameters of the synth could be controlled externally with data, thus 'sonifying' the data!
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Kuramoto Model and Oscillatory Networks
Image Recall
Implemented an image retrieval(memory recall) task using the Kuramoto Model. Extended this to try out graph coloring using Oscillatory Networks.
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