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What's the difference between a market order and limit order? Is one better than the other? With a market order the customer instructs his or her brokerage firm to buy or sell a stock at whatever the price is when the trade is executed, presumably as soon as possible. If the price of the stock is moving quickly and there is a delay in the transmission of the order, then the price at which the customer purchases or sells the stock may be very different than what the customer expected when the order was placed. With a limit order, the customer specifies the price at which he or she is willing to buy or sell. Limit orders can help protect customers from rapid price changes when markets are moving fast. However, there is the risk that the limit order will not be executed. Also note that limit orders usually cost a bit more than market orders.
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Accepted
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Krishna Subramani, Preeti Rao Alexandre D'Hooge ICASSP 2020 Paper / Code / Audio Examples / DOI / BibTeX 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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Krishna Subramani, Srivatsan Sridhar, Rohit M A, Preeti Rao National Conference on Communications 2018 Paper / DOI / BibTeX 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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Krishna Subramani, Alexandre D'Hooge, Preeti Rao ISMIR 2019 Late Breaking/Demo Abstract / 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 |
Submitted
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HaDi Maboudi, Krishna Subramani, Hamid Soltanian-Zadeh, Shun-ichi Amari, Hideaki Shimazaki Submitted to Neural Networks, Under Review Preprint / bioRxiv / BibTeX We introduce a generative model for phase in visual systems, and propose a complex domain based maximum likelihood estimation procedure for parameter estimation. We derive analytical gradient expressions for maximum likelihood estimation using Wirtinger Calculus (detailed in our supplementary material) I presented the initial part of this work as an oral presentation at Honda Research Institute |
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Report / Repository / 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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Presentation / Report / 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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Report / 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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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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The Master Yoda to us Padawans. |