DoppelGANger VS FAST-RIR

Compare DoppelGANger vs FAST-RIR 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)
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DoppelGANger FAST-RIR
14 1
275 137
- -
3.8 5.0
6 months ago 6 months ago
Python Python
BSD 3-clause Clear License GNU Affero General Public License v3.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

DoppelGANger

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

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.

What are some alternatives?

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

CelebV-HQ - [ECCV 2022] CelebV-HQ: A Large-Scale Video Facial Attributes Dataset

CTGAN - Conditional GAN for generating synthetic tabular data.

tdk-demo - This is a collection of TDK demo projects that use different databases and options

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

gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.

SDGym - Benchmarking synthetic data generation methods.

ydata-synthetic - Synthetic data generators for tabular and time-series data

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.