Research References

Chirplet And Chirp Analysis

• Steve Mann, The Chirplet Transform: A new signal analysis technique based on affine relationships in the time-frequency plane: https://www.media.mit.edu/publications/the-chirplet-transform-a-new-signal-analysis-technique-based-on-affine-relationships-in-the-time-frequency-plane/
• General linear chirplet transform overview: https://www.sciencedirect.com/science/article/pii/S0888327015003994
• High-resolution chirplet transform parameter analysis: https://arxiv.org/abs/2108.00572
• Fast Chirplet Transform with FPGA-based implementation: https://www.ece.iit.edu/~eoruklu/IIT/Publications_files/04639630.pdf
• Speed-optimized fast chirplet implementation: https://ecasp.ece.iit.edu/publications/2012-present/2023-13.pdf

Chirp Spread Spectrum / LoRa Receiver Shape

• SDR-LoRa design notes, including synchronization via dechirp and FFT: https://www.sciencedirect.com/science/article/pii/S1389128624000264
• OpenLoRa demodulator evaluation: https://openlora.wisc.edu/demodulators-evaluated/
• OpenLoRa NSDI paper: https://www.usenix.org/system/files/nsdi23-mishra.pdf
• CSS receiver design: https://arxiv.org/abs/2105.02833
• LoRa/CSS tutorial: https://arxiv.org/abs/2310.10503
• I/Q CSS with coherent detector and channel estimation: https://arxiv.org/abs/2009.10421
• Data-over-audio reference implementation trail: https://github.com/cawfree/OpenChirp

Passive Audio Timing / GCC-PHAT

• Improved GCC-PHAT weighting for time-delay estimation: https://pmc.ncbi.nlm.nih.gov/articles/PMC9571281/
• Acoustic DoA paper noting GCC-PHAT normalization and interpolation: https://pmc.ncbi.nlm.nih.gov/articles/PMC11014389/
• Parametrized GCC-PHAT features for time-delay estimation: https://www.isca-archive.org/interspeech_2021/salvati21_interspeech.html
• Complexity/accuracy of GCC-PHAT methods: https://arxiv.org/abs/1811.11787

Low-Level Transform / Compute References

• HLSL Shader Model 6 wave intrinsics: https://learn.microsoft.com/windows/win32/direct3dhlsl/hlsl-shader-model-6-0-features-for-direct3d-12
• Microsoft Kernel Streaming / AVStream overview: https://learn.microsoft.com/en-gb/windows-hardware/drivers/stream/kernel-streaming
• DXGI shared resource handle creation: https://learn.microsoft.com/en-us/windows/win32/api/dxgi1_2/nf-dxgi1_2-idxgiresource1-createsharedhandle
• DirectX Shader Compiler wave intrinsics notes: https://github.com/microsoft/directxshadercompiler/wiki/wave-intrinsics
• cuFFT callback documentation: https://docs.nvidia.com/cuda/archive/12.5.0/cufft/index.html
• NVIDIA cuFFT callback performance article: https://developer.nvidia.com/blog/cuda-pro-tip-use-cufft-callbacks-custom-data-processing/
• FFTW reference: https://www.fftw.org/fftw2_doc/fftw_3.html
• FFTW SIMD alignment: https://www.fftw.org/~fftw/doc/SIMD-alignment-and-fftw_005fmalloc.html
• Chirp Z-transform theory: https://pyffs.readthedocs.io/en/stable/theory/CZT.html

Volumetric Audio / Room Acoustics / Actuation

• Pyroomacoustics beamforming module: https://pyroomacoustics.readthedocs.io/en/pypi-release/pyroomacoustics.beamforming.html
• Pyroomacoustics repository and room/array algorithm examples: https://github.com/LCAV/pyroomacoustics
• Pyroomacoustics paper: https://arxiv.org/abs/1710.04196
• ManyEars sound source localization, tracking, and separation: https://github.com/introlab/manyears
• Spatial Audio Framework, open C/C++ spatial-audio toolkit: https://github.com/leomccormack/Spatial_Audio_Framework
• Sound field reconstruction in reverberant environments with rigid spherical microphone arrays: https://journals.sagepub.com/doi/10.1177/14613484261447569
• Sparse Reconstruction of Sound Field Using Bayesian Compressive Sensing and Equivalent Source Method: https://pmc.ncbi.nlm.nih.gov/articles/PMC10301025/
• Near-field acoustic holography with compressive sensing: https://docs.lib.purdue.edu/herrick/208/
• Ambisonic encoding from equatorial microphone arrays: https://arxiv.org/abs/2211.00584
• Spheroidal ambisonics: https://arxiv.org/abs/2103.05719
• Real-time spherical array renderer: https://research.chalmers.se/publication/509494
• Farrow fractional delay overview: https://engee.com/helpcenter/stable/en/interactive-scripts/dsp/fractional_delay.html
• AMD Vitis fractional-delay Farrow filter note: https://docs.amd.com/r/2024.1-English/Vitis-Tutorials-AI-Engine-Development/Fractional-Delay-Farrow-Filter
• Faust delay/fractional delay reference: https://ringbuffer.org/faust/faust_basics/faust-delay/
• MATLAB Farrow fractional delay example: https://www.mathworks.com/help/dsp/ug/fractional-delay-filters-using-farrow-structures.html
• Joint SFO estimation and compensation based on Farrow structure: https://arxiv.org/abs/2503.07577
• Joint SFO estimation and compensation algorithms based on Farrow structure: https://arxiv.org/abs/2603.00627

Realtime Gaussian Splatting / Dynamic Visual Fields

• Official 3D Gaussian Splatting implementation: https://github.com/graphdeco-inria/gaussian-splatting
• 3D Gaussian Splatting paper: https://arxiv.org/abs/2308.04079
• 4D Gaussian Splatting implementation: https://github.com/fudan-zvg/4d-gaussian-splatting
• 4D Gaussian Splatting paper: https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_4D_Gaussian_Splatting_for_Real-Time_Dynamic_Scene_Rendering_CVPR_2024_paper.pdf
• Spacetime Gaussian Feature Splatting implementation: https://github.com/oppo-us-research/SpacetimeGaussians
• Spacetime Gaussian Feature Splatting paper: https://arxiv.org/abs/2312.16812
• PlayCanvas engine, an open WebGL/WebGPU engine with Gaussian splatting support: https://github.com/playcanvas/engine
• PlayCanvas generic Gaussian splat processing docs: https://developer.playcanvas.com/user-manual/gaussian-splatting/building/unified-rendering/splat-processing/
• NVIDIA vk_gaussian_splatting technical blog: https://developer.nvidia.com/blog/real-time-gpu-accelerated-gaussian-splatting-with-nvidia-designworks-sample-vk_gaussian_splatting/
• gsplat documentation: https://docs.gsplat.studio/main/
• gsplat rasterization API: https://docs.gsplat.studio/main/apis/rasterization.html
• gsplat paper: https://arxiv.org/abs/2409.06765
• StopThePop sorted Gaussian splatting implementation: https://github.com/r4dl/StopThePop
• FlashGS paper: https://arxiv.org/abs/2408.07967
• Gaussian-LIC, Gaussian splatting plus LiDAR-inertial-camera SLAM: https://arxiv.org/abs/2404.06926
• RMGS-SLAM, real-time multi-sensor Gaussian splatting SLAM: https://arxiv.org/abs/2604.12942
• Multi-Calib, online multi-sensor calibration direction: https://pmc.ncbi.nlm.nih.gov/articles/PMC12693731/

How These References Steer Mimir

• Dechirp-plus-bin scoring is the right hot active receiver shape for controlled chirps.
• Dense generic chirplet transforms are research/reference tools, not the runtime hot loop unless the codebook stops being controlled.
• GCC-PHAT is still valuable for passive relative delay, but it must remain an evidence source with confidence, not a canonical timestamp source.
• GPU or native FFT paths should be considered only where batching is real; for a small fixed bin bank, SIMD Goertzel/dechirp may beat FFT overhead.
• If a transform path moves to GPU, fuse dechirp/window/preprocess with transform load/store where possible to avoid memory bandwidth waste.
• Visual fusion should borrow Gaussian splatting data/layout ideas, but the live Mimir problem is online synchronized evidence update, not offline novel-view training from a static capture folder.