pyroomacoustics | audio signal processing for indoor applications | Machine Learning library

by LCAV Python Version: 0.7.5 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pyroomacoustics Summary

pyroomacoustics is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. pyroomacoustics has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install pyroomacoustics' or download it from GitHub, PyPI.

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.

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pyroomacoustics has a medium active ecosystem.

It has 1131 star(s) with 395 fork(s). There are 41 watchers for this library.

There were 2 major release(s) in the last 6 months.

There are 48 open issues and 141 have been closed. On average issues are closed in 66 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pyroomacoustics is 0.7.5

Quality

pyroomacoustics has 0 bugs and 0 code smells.

Security

pyroomacoustics has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pyroomacoustics code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pyroomacoustics is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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pyroomacoustics releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

pyroomacoustics saves you 6891 person hours of effort in developing the same functionality from scratch.

It has 14284 lines of code, 747 functions and 136 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pyroomacoustics and discovered the below as its top functions. This is intended to give you an instant insight into pyroomacoustics implemented functionality, and help decide if they suit your requirements.

Plot the image
Calculate the response of the vehicle
Return the steering vector for a 2D radius
Compute the distance between two matrices
Generate a matplotlib plot of the spectrum
Calculate the polar distance between two vectors
Generate a dirty image
Convert polar to cartesian coordinates
Generate a matplotlib plot of the source spectrum
Perform a trinicon filter
R Derivative Fourier Transform
Demo - rrma decomposition
R Compute a sparse auxiliary auxiliary function
Measure the IR volume
Plot the IR spectrum
Initialize a cylinder from corners
Rotate a source wave function
Simulate a sound
R Compute the Dirac system of Dirac
Runs the analysis
R Determines the inner product of a dirac
Implementation of dirac_recon_recon
Perform Diracreconization on a set of bands
Resolve a pt_src
Fit the EM algorithm
Performs Diracre correlation on Dirac_recon

Get all kandi verified functions for this library.

pyroomacoustics Key Features

No Key Features are available at this moment for pyroomacoustics.

pyroomacoustics Examples and Code Snippets

No Code Snippets are available at this moment for pyroomacoustics.

Community Discussions

Trending Discussions on pyroomacoustics

Different results from PyTorch's conv1d and SciPy's convolve

QUESTION

Different results from PyTorch's conv1d and SciPy's convolve

Asked 2020-Feb-24 at 15:35

I'm building a PyTorch model to estimate Impuse Responses. Currently I am calculating the loss from the real and estimated impulse response. I would like to convolve both the estimated and real impulse response with a signal and then calculate the loss from those.

The pyroomaccoustics package uses SciPy's fftconvolve to convolve the impulse response with a given signal. I cannot use this since it would break PyTorch's computation graph. PyTorch's conv1d uses cross-correlation. From this answer it seems that by flipping the filter conv1d can be used for convolution.

I am confused as to why the following code gives a different result for conv1d and convolve and what must be changed to get the outputs to be equal.

...

ANSWER

Answered 2020-Feb-24 at 15:35

Take a look at the mode parameter of scipy.signal.convolve. Use mode='valid' to match PyTorch's conv1d:

Source https://stackoverflow.com/questions/60377320

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pyroomacoustics

You can install using 'pip install pyroomacoustics' or download it from GitHub, PyPI.
You can use pyroomacoustics like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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