pyroomacoustics
nnAudio
| pyroomacoustics | nnAudio | |
|---|---|---|
| 2 | 1 | |
| 1,327 | 955 | |
| 1.4% | - | |
| 3.5 | 5.3 | |
| 10 days ago | 3 months ago | |
| Python | Python | |
| MIT License | MIT License |
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pyroomacoustics
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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
nnAudio
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LEAF: A Learnable Frontend for Audio Classification
FYI nnAudio has learnable STFT and Mel kernels https://github.com/KinWaiCheuk/nnAudio
What are some alternatives?
pydub - Manipulate audio with a simple and easy high level interface
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
beets - music library manager and MusicBrainz tagger
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
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.
kapre - kapre: Keras Audio Preprocessors
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.