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)
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. (by LCAV)
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FAST-RIR | pyroomacoustics | |
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1 | 2 | |
137 | 1,325 | |
- | 3.0% | |
5.0 | 3.5 | |
6 months ago | 3 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | MIT License |
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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.
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.
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[ICASSP 2022] FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR
Code: https://github.com/anton-jeran/FAST-RIR
pyroomacoustics
Posts with mentions or reviews of pyroomacoustics.
We have used some of these posts to build our list of alternatives
and similar projects.
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How would you classify impulse response and convolution?
1) For the legal aspects, you could find some dataset with open licenses. Or, you could simulate the room impulse responses with a simulation tool like pyroomacoustics (disclaimer: I am the main developer).
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physically modeling reverb
I am developing such a simulation tool in python called pyroomacoustics. It is similar to wayverb linked in a different comment, but can be operated in python and is probably easier to get started with. https://github.com/LCAV/pyroomacoustics
What are some alternatives?
When comparing FAST-RIR and pyroomacoustics you can also consider the following projects:
CTGAN - Conditional GAN for generating synthetic tabular data.
pydub - Manipulate audio with a simple and easy high level interface
DoppelGANger - [IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
beets - music library manager and MusicBrainz tagger
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
SDGym - Benchmarking synthetic data generation methods.