SameSamp

Click picture for youtube video explaination:

Web Application

Introducing SameSamp, a web application that helps music producers and record labels decide if a sample is worth using in their song. Gain insights on how similar songs with the same sample performed.

Same-Samp/client

React client side

To test client side follow these steps:

1. Open a terminal or command prompt.

2. Navigate to the Same-Samp/client directory.

3. Run the following command to install the required dependencies:

   npm install

4. Once the installation is complete, start the development server by running the following command:

   npm start

   This will start the React app and open it in your default browser.

5. You can now access the React app at http://localhost:3000 in your browser.

Make sure you have Node.js and npm installed on your system before running the above commands.

Same-Samp/Backend

The backend is built with Flask, a Python web framework. The server receives requests to generate a new graph and creates a new graph object. It then runs the selected algorithm on the graph to determine the most similar songs. The results are displayed, providing users with a list of songs that are deemed similar.

To test client side follow these steps:

1. Open a terminal or command prompt If you want to run with client make sure to open a NEW terminal.

2. Navigate to the Same-Samp/client directory.

   cd Same-Samp/Backend

3. Start the server with this command

   python server.py

Graph Class

The Graph class in the Same-Samp/Backend directory is responsible for representing the graph structure. It provides methods for adding vertices, adding edges between vertices, and other operations on the graph. Additionally in the graph are the two algorithms: Djikstra's and BFS which can be called after instatiated a graph based of the sample selection. The edges of the graph are made by the following calculation below

Dataset

The dataset used in this project was made by combining data scraped from WhoSampled's top sampled songs list and the spotify API. After scraping all of the songs that used the top 20 most popular samples, the data was used to call Spotify's API to get different metrics about the songs. In total, there are around 75k songs.

Artist	SongName	SpotifyID	Genre	Year	Popularity	Duration (ms)	Explicit	Avail_Markets	PlayCount	GrossRev	Age	annualRev
N.W.A.	Straight Outta Compton	6...W	Hip-Hop / Rap / R&B	1988	74	258687	True	['AR'...]	4922929	9845	35	12300.625
Tyler, The Creator	Pigs	5...J	Hip-Hop / Rap / R&B	2013	54	254733	True	['AR', US ...]	8325632	16651	10	2081.375
Skrillex	Scary Monsters and Nice Sprites	4...0	Electronic / Dance	2011	61	243353	False	['AT', US', ...]	6751477	13502	12	1687.75

Spotify Data Collector

Calls the spotify API and then iterates over the list of songs scraped from WhoSampled to gather more attributes about the songs such as popularity, playcount and available markets.

Data Cleaner

The data cleaner is responsible for preprocessing and cleaning the collected data. This step involves tasks such as removing duplicates, handling missing values, normalizing data, and transforming it into a suitable format for further analysis.

Random Forest Regression Model (Work In Progress)

This is a regression model that is using both numerical and categorical values to predict the revenue of a new song that was inputted into the graph. The categorical values are:

1. Genre: This represents the genre of the songs that sampled other songs. For example, hip-hop is notorious for sampling other older funk / soul songs.
2. Instrument: This indicates the type of instrument sampled from the original song. This is important because, for example, vocal samples can be more expensive to clear but can also make a song significantly more successful.

Setting up the Environment

To run the notebook, you will need to install Conda, create a Conda environment, and install Jupyter Notebook. Please follow the steps below:

Step 1: Install Conda

First, you need to install Conda if you haven't already. You can choose between Anaconda and Miniconda. Anaconda includes a lot of the data science packages along with Conda, while Miniconda includes only Conda and Python and generally considered when dealing with systems that have limited resources.

• install Anaconda, Anaconda Downloads
• install Miniconda, Miniconda Downloads

Step 2: Create a Conda Environment

After installing Conda, you can create a Conda environment using the terminal (on Linux or macOS) or Anaconda Prompt (on Windows). Replace myenv with your preferred name for the environment.

conda create --name myenv

conda activate myenv

Step 3: Install Jupyter Notebook

conda install jupyter

Step 4: Navigate to Regression Folder

From the base Sampe Samp Folder

cd ./Regression

Once you are in the Regression folder run this command:

jupyter notebook

After your browser should open the notebook where you can run it. Additionally you may need to install the packages used in the notebook.

Generally that process goes like:

conda install package_name

But it is best practice to look up how the package is installed in conda as it can occasionally vary from the package name