FAST-RIR VS SDGym

Compare FAST-RIR vs SDGym and see what are their differences.

FAST-RIR

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. (by anton-jeran)
room-impulse-response automatic-speech-recognition acoustics conditional-generation generative-adversarial-network Speech Augmentation synthetic-data rir neural-network diffuse-scattering Deep Learning impulse-response implicit-neural-representation
Source Code
anton-jeran.github.io
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SDGym

Benchmarking synthetic data generation methods. (by sdv-dev)
synthetic-data Benchmark tabular-data synthetic-data-vault sdgym-synthesizers generative-adversarial-network Deep Learning generative-ai generative-models
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FAST-RIR SDGym
1 1
137 242
- 4.5%
5.0 7.5
6 months ago 4 days ago
Python Python
GNU Affero General Public License v3.0 GNU General Public License v3.0 or later
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FAST-RIR

Posts with mentions or reviews of FAST-RIR. We have used some of these posts to build our list of alternatives and similar projects.

SDGym

Posts with mentions or reviews of SDGym. We have used some of these posts to build our list of alternatives and similar projects.
  • [D] Synthetic data generation techniques for data privacy
    1 project | /r/MachineLearning | 15 Feb 2022
    I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.

What are some alternatives?

When comparing FAST-RIR and SDGym you can also consider the following projects:

CTGAN - Conditional GAN for generating synthetic tabular data.

Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.

DoppelGANger - [IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

SDV - Synthetic data generation for tabular data

caer - High-performance Vision library in Python. Scale your research, not boilerplate.

Copulas - A library to model multivariate data using copulas.

pyroomacoustics - Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.

DPL - [NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

FAST-RIR vs CTGAN SDGym vs Mimesis FAST-RIR vs DoppelGANger SDGym vs SDV FAST-RIR vs caer SDGym vs Copulas FAST-RIR vs pyroomacoustics SDGym vs DPL