mcse | channel speech enhancement system | Speech library

 by   DistantSpeechRecognition Python Version: Current License: No License

kandi X-RAY | mcse Summary

kandi X-RAY | mcse Summary

mcse is a Python library typically used in Artificial Intelligence, Speech applications. mcse has no bugs, it has no vulnerabilities and it has low support. However mcse build file is not available. You can download it from GitHub.

This is a multi-channel speech enhancement system implementing MVDR beamformer optionally followed by 5 different post-filters:. The main function is multichannel_speech_enhancement.py.
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            kandi-support Support

              mcse has a low active ecosystem.
              It has 74 star(s) with 46 fork(s). There are 10 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. On average issues are closed in 1238 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of mcse is current.

            kandi-Quality Quality

              mcse has 0 bugs and 0 code smells.

            kandi-Security Security

              mcse has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              mcse code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              mcse does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              mcse releases are not available. You will need to build from source code and install.
              mcse has no build file. You will be need to create the build yourself to build the component from source.
              It has 332 lines of code, 15 functions and 12 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed mcse and discovered the below as its top functions. This is intended to give you an instant insight into mcse implemented functionality, and help decide if they suit your requirements.
            • Compute the noise field coherence matrix
            • R Calculates the time delay of an orbit
            • Calculates the frequencies of the Fourier transform
            • Compute the Sparse Time Fourier Transform
            • Return the next pow2
            • R Calculate the free field of a propagation vector
            Get all kandi verified functions for this library.

            mcse Key Features

            No Key Features are available at this moment for mcse.

            mcse Examples and Code Snippets

            No Code Snippets are available at this moment for mcse.

            Community Discussions

            QUESTION

            Comparing R and Python Vectorization and Optimization
            Asked 2021-Oct-15 at 19:40

            In the R language, optimization can be achieved by using purrr::map() or furrr::future_map() functions. However, I am not sure how does optimization works for np.array() methods. Indeed, I would like to understand how does Python and R scales out to parallel processing [1, 2] in terms of complexity and performance.

            Thus, the following questions arise:

            How does the optimization of np.array() in Python works comparing to purrr::map() and furrr::future_map() functions in the R language?

            By doing a simple tictoc test on purrr/furrr, I can observe that we have a big win from vectorization in both cases. Nonetheless, I can also notice that the results seem to show that the R language is just fundamentally faster.

            Python ...

            ANSWER

            Answered 2021-Oct-15 at 19:40

            I believe numpy wraps some of its "primitive" objects in wrapper classes which are, themselves, Python (eg. this one). When looking at the R mirror source, I conversely find an array class that's basically native code (aka C). That extra indirection layer alone could explain the difference in speed, I guess.

            Source https://stackoverflow.com/questions/69498290

            QUESTION

            Why does this book suggest you can overload javascript constructors?
            Asked 2020-May-27 at 22:14

            While I'm not a Javascript expert, I'm pretty familiar with it and have used it for years. As far as I know there is no ability to overload functions or constructors (from a language support perspective; of course you can basically simulate it).

            So I want to get a MCSE and going through a study book for one of the exams I will take and I see what's included in the attached image. It appears as though the book is suggesting Javascript would choose the correct constructor (the one with no parameters, or the one with 3 parameters) based on whether you call it with 0 or 3 parameters. Is that right? I didn't think that Javascript did that. What am I missing here?

            ...

            ANSWER

            Answered 2020-May-27 at 22:14

            That defines Book() and then immediately redefines it, ignoring the previous definition. I'm not sure why you'd do that, and given how out of date this code is, ES6 introduces class which makes this all irrelevant, it may be a merely academic point now.

            For example:

            Source https://stackoverflow.com/questions/62053689

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

            Vulnerabilities

            No vulnerabilities reported

            Install mcse

            You can download it from GitHub.
            You can use mcse 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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          • HTTPS

            https://github.com/DistantSpeechRecognition/mcse.git

          • CLI

            gh repo clone DistantSpeechRecognition/mcse

          • sshUrl

            git@github.com:DistantSpeechRecognition/mcse.git

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