@article{chimefrb:systemOverview:2018,
author = {The CHIME/FRB Collaboration},
title = {The {CHIME} Fast Radio Burst Project: System Overview},
journal = {The Astrophysical Journal},
url = {https://doi.org/10.3847%2F1538-4357%2Faad188},
year = 2018,
month = {Aug.},
publisher = {American Astronomical Society},
volume = {863},
number = {1},
pages = {48},
abstract = {The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a novel transit radio telescope operating across the 400–800 MHz band. CHIME is composed of four 20 m × 100 m semicylindrical paraboloid reflectors, each of which has 256 dual-polarization feeds suspended along its axis, giving it a ≳200 deg2 field of view. This, combined with wide bandwidth, high sensitivity, and a powerful correlator, makes CHIME an excellent instrument for the detection of fast radio bursts (FRBs). The CHIME Fast Radio Burst Project (CHIME/FRB) will search beam-formed, high time and frequency resolution data in real time for FRBs in the CHIME field of view. Here we describe the CHIME/FRB back end, including the real-time FRB search and detection software pipeline, as well as the planned offline analyses. We estimate a CHIME/FRB detection rate of 2–42 FRBs sky–1 day–1 normalizing to the rate estimated at 1.4 GHz by Vander Wiel et al. Likely science outcomes of CHIME/FRB are also discussed. CHIME/FRB is currently operational in a commissioning phase, with science operations expected to commence in the latter half of 2018.}
}
@InProceedings{chowdhury:physModelTape:2019,
author = {Jatin Chowdhury},
title = {Real-Time Physical Modelling For Analog Tape Machines},
booktitle = {22nd International Conference on Digital Audio Effects},
year = {2019},
pages = {3},
address = {Birmingham, UK},
url = {http://dafx.de/paper-archive/2019/DAFx2019_paper_3.pdf}
}
@misc{chowdhury:frbCosmo:2019,
author = {Jatin Chowdhury},
title = {Cosmological Implications of Fast Radio Bursts},
year = {2019},
month = {Dec.},
url = {https://ccrma.stanford.edu/~jatin/papers/frb_cosmo.pdf},
howpublished = {\url{https://ccrma.stanford.edu/~jatin/papers/frb_cosmo.pdf}}
}
@InProceedings{yang:chowdhury:Cassettes:2019,
author = {Xingxing Yang and Jatin Chowdhury},
title = {Cassettes - An Online Audio Editor},
booktitle = {Web Audio Conference 2019},
year = {2019},
month = {Dec.},
pages = {2},
address = {Trondheim, Norway},
url = {https://www.ntnu.edu/documents/1282113268/1290817988/WAC2019-CameraReadySubmission-20.pdf/c4ccc750-691f-37d5-dbe1-4bea76631946?t=1575408892147}
}
@misc{chowdhury:complexNL:2020,
author = {Jatin Chowdhury},
title = {Complex Nonlinearities for Audio Signal Processing},
year = {2020},
month = {Feb.},
url = {https://ccrma.stanford.edu/~jatin/papers/Complex_NLs.pdf},
howpublished = {\url{https://ccrma.stanford.edu/~jatin/papers/Complex_NLs.pdf}}
}
@misc{chowdhury:badCircuits:2020,
author = {Jatin Chowdhury},
title = {Bad Circuit Modelling},
year = {2020},
month = {June},
url = {https://ccrma.stanford.edu/~jatin/papers/BadCircuitModels.pdf},
howpublished = {\url{https://ccrma.stanford.edu/~jatin/papers/BadCircuitModels.pdf}}
}
@InProceedings{chowdhury:nonlinearBiquad:2020,
author = {Jatin Chowdhury},
title = {Stable Structures for Nonlinear Biquad Filters},
booktitle = {23nd International Conference on Digital Audio Effects},
year = {2020},
pages = {3},
address = {Vienna, AT},
url = {https://dafx2020.mdw.ac.at/proceedings/papers/DAFx2020_paper_3.pdf},
}
@InProceedings{chowdhury:rau:canfield-dafilou:waterbottles:2020,
author = {Jatin Chowdhury and Elliot Canfield-Dafilou and Mark Rau},
title = {Water Bottle Synthesis with Modal Signal Processing},
booktitle = {23nd International Conference on Digital Audio Effects},
year = {2020},
pages = {24},
address = {Vienna, AT},
url = {https://dafx2020.mdw.ac.at/proceedings/papers/DAFx2020_paper_24.pdf},
}
@misc{chowdhury:klon:2020,
title={A Comparison of Virtual Analog Modelling Techniques for Desktop and Embedded Implementations},
author={Jatin Chowdhury},
year={2020},
eprint={2009.02833},
archivePrefix={arXiv},
primaryClass={eess.AS},
url = {https://arxiv.org/pdf/2009.02833.pdf}
}
@misc{chowdhury2021rtneural,
title={RTNeural: Fast Neural Inferencing for Real-Time Systems},
author={Jatin Chowdhury},
year={2021},
eprint={2106.03037},
archivePrefix={arXiv},
primaryClass={eess.AS},
url = {https://arxiv.org/pdf/2106.03037.pdf}
}
@InProceedings{roosenburg:stine:michon:chowdhury:wdmodels:2021,
author = {Dirk Roosenburg and Eli Stine and Romain Michon and Jatin Chowdhury},
title = {A Wave Digital Filter Modeling Library for the Faust Programming Language},
booktitle = {18th Sound and Music Computing Conference},
year = {2021},
pages = {24-30},
url = {https://zenodo.org/record/5045088},
}
@InProceedings{chowdhury:clarke:diffwdfs:2022,
author = {Jatin Chowdhury and Christopher Johann Clarke},
title = {Emulating Diode Circuits with Differentiable Wave Digital Filters},
booktitle = {19th Sound and Music Computing Conference},
year = {2022},
pages = {2-9},
url = {https://zenodo.org/record/6566846},
}
@misc{chowdhury2022chowdspwdf,
title = {chowdsp_wdf: An Advanced C++ Library for Wave Digital Circuit Modelling},
author = {Jatin Chowdhury},
year = {2022},
eprint={2210.12554},
archivePrefix={arXiv},
primaryClass={eess.AS},
doi = {10.48550/ARXIV.2210.12554},
url = {https://arxiv.org/abs/2210.12554.pdf},
}
@inproceedings{10.1145/3538641.3561501,
author = {Clarke, Christopher Johann and Chowdhury, Jatin and Lin, Cindy Ming Ying and Tan, Ivan Fu Xing and Priyadarshinee, Prachee and BT, Balamurali and Herremans, Dorien and Chen, Jer-Ming},
title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
year = {2022},
isbn = {9781450393980},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3538641.3561501},
doi = {10.1145/3538641.3561501},
abstract = {This paper puts forth a method that is easily transposable to a realtime environment by utilising a "Physics Approximating Neural Network" to predict 1D output signal. The technique described in this paper is inspired by Physics Informed Neural Networks put forth by Raissi et al. The model demonstrated in this paper makes use of a recurrent input. This is passed to a "preprocessing" stage of 2 layers by 8 neurons wide. The output of the preprocessing stage is passed to the "approximation layer". Lastly, the output of the "approximation layer" is passed to a "postprocessing" layer of 5 layers by 8 neurons wide. The architecture of the model is explained and tested on a specially developed dataset. The results show closer similarity to the ground truth compared to a linear model or a dense-layer neural network.},
booktitle = {Proceedings of the Conference on Research in Adaptive and Convergent Systems},
pages = {139–146},
numpages = {8},
keywords = {physical model, neural networks, real-time systems},
location = {Virtual Event, Japan},
series = {RACS '22}
}