Vern  Greenholt

Vern Greenholt

1594954920

Breaking Spotify’s Algorithm of Music Genre Classification!

Introduction

There are many different types of genres present in the industry. But the basic genres will have a few principle aspects that make it easier to identify them. Genres are used to tag and define different kinds of music based on the way they are composed or based on their musical form and musical style.

In this article, you will learn to build your own model which will take in a song as an input and predict or classify that particular song in one of the genres. We will be classifying among the following basic genres — blues. classical, country, disco, hip hop, jazz, metal, pop, reggae and rock. The model will be build using LSTM networks. Don’t worry if you do not know what LSTM is. This article will give you a brief understanding of LSTM and its working.

Here is the GitHub link to the entire project — https://github.com/rajatkeshri/Music-Genre-Prediction-Using-RNN-LSTM

The entire article is divided into 4segments —

  1. Prerequisites
  2. Theory
  3. Data Preprocessing
  4. Training the model
  5. Predicting on new data

Prerequisites

There are a few prerequisites you will need to have before you start this project. The first thing you would require is the dataset. The music data which I have used for this project can be downloaded from kaggle — https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification.

Note that this dataset contains 10 classes with 100 songs withing each class. This might sound to be very less for a machine learning project, that is why in the next section I will show you how to increase the number of training data for each class of genre.

There are a few modules which will be required for you to install in your PC/laptop in order to get started. We will be building the entire LSTM model using Tensorflow, coded in python. We will be working with python 3.6 or higher (If you are using python 2.7, it is required for you to use python 3.6 or higher for full support and functionality). The following are the required python packages to be installed —

  1. Tensorflow — Machine learning library
  2. librosa — Speech processing library to extract features from songs
  3. numpy — Mathematical model for scientific computing
  4. sklrean — Another machine learning model (We will use this library to split training and testing data)
  5. json — To jsonify the dataset (Explained in the next section)
  6. pytdub — To convert mp3 to wav files

These modules can be installed using pip or conda. You can find many online sources and youtube videos on getting started with pip or conda. Once the above modules are installed, let’s get coding!

#spotify #mfcc #music-genre #classification #lstm #algorithms

What is GEEK

Buddha Community

Breaking Spotify’s Algorithm of Music Genre Classification!
Vern  Greenholt

Vern Greenholt

1594954920

Breaking Spotify’s Algorithm of Music Genre Classification!

Introduction

There are many different types of genres present in the industry. But the basic genres will have a few principle aspects that make it easier to identify them. Genres are used to tag and define different kinds of music based on the way they are composed or based on their musical form and musical style.

In this article, you will learn to build your own model which will take in a song as an input and predict or classify that particular song in one of the genres. We will be classifying among the following basic genres — blues. classical, country, disco, hip hop, jazz, metal, pop, reggae and rock. The model will be build using LSTM networks. Don’t worry if you do not know what LSTM is. This article will give you a brief understanding of LSTM and its working.

Here is the GitHub link to the entire project — https://github.com/rajatkeshri/Music-Genre-Prediction-Using-RNN-LSTM

The entire article is divided into 4segments —

  1. Prerequisites
  2. Theory
  3. Data Preprocessing
  4. Training the model
  5. Predicting on new data

Prerequisites

There are a few prerequisites you will need to have before you start this project. The first thing you would require is the dataset. The music data which I have used for this project can be downloaded from kaggle — https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification.

Note that this dataset contains 10 classes with 100 songs withing each class. This might sound to be very less for a machine learning project, that is why in the next section I will show you how to increase the number of training data for each class of genre.

There are a few modules which will be required for you to install in your PC/laptop in order to get started. We will be building the entire LSTM model using Tensorflow, coded in python. We will be working with python 3.6 or higher (If you are using python 2.7, it is required for you to use python 3.6 or higher for full support and functionality). The following are the required python packages to be installed —

  1. Tensorflow — Machine learning library
  2. librosa — Speech processing library to extract features from songs
  3. numpy — Mathematical model for scientific computing
  4. sklrean — Another machine learning model (We will use this library to split training and testing data)
  5. json — To jsonify the dataset (Explained in the next section)
  6. pytdub — To convert mp3 to wav files

These modules can be installed using pip or conda. You can find many online sources and youtube videos on getting started with pip or conda. Once the above modules are installed, let’s get coding!

#spotify #mfcc #music-genre #classification #lstm #algorithms

Mike  Kozey

Mike Kozey

1656151740

Test_cov_console: Flutter Console Coverage Test

Flutter Console Coverage Test

This small dart tools is used to generate Flutter Coverage Test report to console

How to install

Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dev_dependencies:
  test_cov_console: ^0.2.2

How to run

run the following command to make sure all flutter library is up-to-date

flutter pub get
Running "flutter pub get" in coverage...                            0.5s

run the following command to generate lcov.info on coverage directory

flutter test --coverage
00:02 +1: All tests passed!

run the tool to generate report from lcov.info

flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
 print_cov_constants.dart                    |    0.00 |    0.00 |    0.00 |    no unit testing|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Optional parameter

If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE>                      The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...>    A list of contains string for files without unit testing
                                       to be excluded from report
-l, --line                             It will print Lines & Uncovered Lines only
                                       Branch & Functions coverage percentage will not be printed
-i, --ignore                           It will not print any file without unit testing
-m, --multi                            Report from multiple lcov.info files
-c, --csv                              Output to CSV file
-o, --output=<CSV-FILE>                Full path of output CSV file
                                       If not given, "coverage/test_cov_console.csv" will be used
-t, --total                            Print only the total coverage
                                       Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM>                   Print only the whether total coverage is passed MINIMUM value or not
                                       If the value >= MINIMUM, it will print PASSED, otherwise FAILED
                                       Note: it will ignore all other option (if any), except -m
-h, --help                             Show this help

example run the tool with parameters

flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

report for multiple lcov.info files (-m, --multi)

It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Output to CSV file (-c, --csv, -o, --output)

flutter pub run test_cov_console -c --output=coverage/test_coverage.csv

#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""

Installing

Use this package as an executable

Install it

You can install the package from the command line:

dart pub global activate test_cov_console

Use it

The package has the following executables:

$ test_cov_console

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add test_cov_console

With Flutter:

 $ flutter pub add test_cov_console

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

dependencies:
  test_cov_console: ^0.2.2

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:test_cov_console/test_cov_console.dart';

example/lib/main.dart

import 'package:flutter/material.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        // This is the theme of your application.
        //
        // Try running your application with "flutter run". You'll see the
        // application has a blue toolbar. Then, without quitting the app, try
        // changing the primarySwatch below to Colors.green and then invoke
        // "hot reload" (press "r" in the console where you ran "flutter run",
        // or simply save your changes to "hot reload" in a Flutter IDE).
        // Notice that the counter didn't reset back to zero; the application
        // is not restarted.
        primarySwatch: Colors.blue,
        // This makes the visual density adapt to the platform that you run
        // the app on. For desktop platforms, the controls will be smaller and
        // closer together (more dense) than on mobile platforms.
        visualDensity: VisualDensity.adaptivePlatformDensity,
      ),
      home: MyHomePage(title: 'Flutter Demo Home Page'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  MyHomePage({Key? key, required this.title}) : super(key: key);

  // This widget is the home page of your application. It is stateful, meaning
  // that it has a State object (defined below) that contains fields that affect
  // how it looks.

  // This class is the configuration for the state. It holds the values (in this
  // case the title) provided by the parent (in this case the App widget) and
  // used by the build method of the State. Fields in a Widget subclass are
  // always marked "final".

  final String title;

  @override
  _MyHomePageState createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  int _counter = 0;

  void _incrementCounter() {
    setState(() {
      // This call to setState tells the Flutter framework that something has
      // changed in this State, which causes it to rerun the build method below
      // so that the display can reflect the updated values. If we changed
      // _counter without calling setState(), then the build method would not be
      // called again, and so nothing would appear to happen.
      _counter++;
    });
  }

  @override
  Widget build(BuildContext context) {
    // This method is rerun every time setState is called, for instance as done
    // by the _incrementCounter method above.
    //
    // The Flutter framework has been optimized to make rerunning build methods
    // fast, so that you can just rebuild anything that needs updating rather
    // than having to individually change instances of widgets.
    return Scaffold(
      appBar: AppBar(
        // Here we take the value from the MyHomePage object that was created by
        // the App.build method, and use it to set our appbar title.
        title: Text(widget.title),
      ),
      body: Center(
        // Center is a layout widget. It takes a single child and positions it
        // in the middle of the parent.
        child: Column(
          // Column is also a layout widget. It takes a list of children and
          // arranges them vertically. By default, it sizes itself to fit its
          // children horizontally, and tries to be as tall as its parent.
          //
          // Invoke "debug painting" (press "p" in the console, choose the
          // "Toggle Debug Paint" action from the Flutter Inspector in Android
          // Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
          // to see the wireframe for each widget.
          //
          // Column has various properties to control how it sizes itself and
          // how it positions its children. Here we use mainAxisAlignment to
          // center the children vertically; the main axis here is the vertical
          // axis because Columns are vertical (the cross axis would be
          // horizontal).
          mainAxisAlignment: MainAxisAlignment.center,
          children: <Widget>[
            Text(
              'You have pushed the button this many times:',
            ),
            Text(
              '$_counter',
              style: Theme.of(context).textTheme.headline4,
            ),
          ],
        ),
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: Icon(Icons.add),
      ), // This trailing comma makes auto-formatting nicer for build methods.
    );
  }
}

Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console 
License: BSD-3-Clause license

#flutter #dart #test 

Create Music Streaming App Like Spotify

Interested in music application development like Spotify? We at AppClues Infotech help to build online music streaming and podcast apps like Spotify for iOS and Android. Hire our best designers & developers to build your own music streaming app like Spotify with customized features & functionality.

For more info:
Website: https://www.appcluesinfotech.com/
Email: info@appcluesinfotech.com
Call: +1-978-309-9910

#create music streaming app like spotify #create music streaming app like spotify #create music streaming app like spotify #hire music streaming app developers #cost to make a music streaming app #cost to make an app like spotify

Gerhard  Brink

Gerhard Brink

1624057020

Tech for Enjoying Music - Here’s How Spotify uses Big Data

Don’t search or scroll for your favorite songs, Spotify will take care of it using Big Data

You’re listening to one of your favorite Jazz songs. And the next song is of the same genre. Gone are the days of downloading your favorite songs. Personalized online streaming is the new thing and Spotify – the largest on-demand music service provider, was the first one to make this breakthrough. What made Spotify different from other music platforms? The company effectively leveraged technology, primarily big data to provide a personalized experience to each user. How Spotify uses Big Data? Let’s know more about it

Spotify online player entered the music industry in 2008 with 24 million registered users (still counting). Having around 20 million songs in its database, they keep on adding 20 thousand new songs every day. That’s quite impressive. With so much data, it makes sense to leverage big data tools and techniques to provide a high-quality user experience.

Spotify breathes data as for each decision they tend to use data. As the platform continues to procure data points, it is using data to train machines and algorithms to listen to music and provide insights that are useful for the experience of its users as well as its business.

#big data #latest news #tech for enjoying music #spotify #spotify uses big data #tech for enjoying music - here’s how spotify uses big data

Ajay Kapoor

1625811540

How to Create a Free Music App like Spotify? Solution Suggest

Music is something that completes our lives. Don’t you think so? Now, with the advent of technology, the physical recording has become a thing of the past. Today, 86 percent of users are listening to music using on-demand streaming services.

Did you know? According to stats, the revenue in the music streaming segment is projected to reach the United States $23,053 million by the end of 2021. It is expected to show an annual growth rate of 9.69%, resulting in an estimated market volume of US $33,372 million by 2025.

These figures show how big the music streaming market is. The demand for music has changed with the advancement of internet connection speed. And this results in the increasing popularity of music streaming apps.

With over 75 million users, Spotify is the most popular of them all. The app has a lot of users, significant revenue figures, and even paid customers. If you want to make a music app like Spotify, then this article is for you. I’ll give you a comprehensive guide on how to make an app like Spotify. You can take the help of mobile app development company in India for app development.

Read the full blog here: How to create app like spotify

#app like spotify #create app like spotify #how to create music app #how to create app like spotify #music #music-app