dcase2019_task1b | DCASE 2019 Task 1b - Acoustic Scene Classification

by OptimusPrimus Python Version: Current License: MIT

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kandi X-RAY | dcase2019_task1b Summary

dcase2019_task1b is a Python library. dcase2019_task1b has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However dcase2019_task1b build file is not available. You can download it from GitHub.

This repository contains CP-JKU Student team's submission for DCASE Challenge 2019. A technical report describing this system will be available on the DCASE homepage as soon as official evaluation results are available. We need to stress that self trained results might differ slightly from the ones described in the report, since we do not seed the random number generator manually. We therefore additionally provide all the files necessary to recreate our submissions in folders tmp/data/{no_da, mse_da_0, mse_da_1, mi_da}. For a detailed description of task, data set, and baseline, see:

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Support

dcase2019_task1b has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

dcase2019_task1b has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dcase2019_task1b is current.

Quality

dcase2019_task1b has no bugs reported.

Security

dcase2019_task1b has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

dcase2019_task1b is licensed under the MIT License. This license is Permissive.

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

Reuse

dcase2019_task1b releases are not available. You will need to build from source code and install.

dcase2019_task1b has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed dcase2019_task1b and discovered the below as its top functions. This is intended to give you an instant insight into dcase2019_task1b implemented functionality, and help decide if they suit your requirements.

Run all folds
Train a model on the model
Performs a single step
Set phase
Compute the mean and variance of a fold
Returns a data loader for the given fold and phase
Return data loader for given fold and phase
Create a ParallelDataSet object from the data set
Forward D
Compute the IIC coefficient
Calculate the logit transform
Forward convolution
Compute the gradient of the forward matrix
Mean squared error

Get all kandi verified functions for this library.

dcase2019_task1b Key Features

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dcase2019_task1b Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install dcase2019_task1b

You can download it from GitHub.
You can use dcase2019_task1b 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.