I have searched all over the internet for the full 280 GB file, and by emailing the million song dataset challenge's owner, I was able to find a single torrent file which worked, however, had only 1 peer. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Don’t Start With Machine Learning. Our dataset contains around 400,000 songs in English. Mode is whether the song uses a major or a minor key in its production. To increase the predictive power of my model, I would like to try further degrees of polynomial transformations to find better interactions. Since Spotify acquired EchoNest, many different features were changed including a simple way to look up song info by ID. Predicting how popular a song will be is no easy task. The dataset was too large as well. A song is never just one audio feature. But I want to split that as rows. While DJ Khaled was ill equipped with powerful data science and machine learning tools, he was correct in that certain trends do exist in hit songs. My failed choices left me seeking to understand if song popularity can be predicted and what that looks like. My model utilizing Lasso feature selection performed the best with an R-squared value of .28 and my explanatory variables were narrowed down to 34. Because of this the demo uses a very roundabout way to grab song info. We decided to use BillBoard Top 100 to determine popularity. Take a look, 3D Object Detection Using Lidar Data for Self Driving Cars, Creating and Deploying a COVID-19 Choropleth Dashboard using Pandas and Plotly/Dash, How I used Python and Data Science to win at Fantasy Golf, Fixing The Biggest Problem of K Means Clustering, The OG Data Scientists: LTCM and Renaissance, Basic Understanding of Data Structure & Algorithms, Timestamps are data gold, and I hate them, Assigning all NaNs for follower count (my API requests were mostly successful but I had to manually look up and hard code in a few), Consolidating genres down from 190 ‘unique’ genres to around 30 genres, Creating dummy variables for each genre and removing the original genre column, Creating a new feature for the total # of words in each title (I thought this may be impactful), Creating a new feature in place of year, ‘years since released’. Every artist in the data was uniquely identified by a string, so we decided to do label encoding on them. Chicago Crime Dataset. Biz & IT — Million-song dataset: take it, it’s free A dataset of the characteristics of one million commercially available songs …. Popular songs secure the lion’s share of revenue. * Please see the paper and the GitHub repository for more information Attribute Information: Nine audio features computed across time and summarized with seven statistics (mean, standard deviation, skew, kurtosis, median, minimum, maximum): 1. The “mashable” dataset in its raw form makes it a regression problem i.e. The dataset chosen was the Million Songs Dataset provided by Columbia University and pulled from Echo Nest. Weighing in at almost 350,000 rows with tons of detail it could be a great resource for those who are wishing to stretch their data science chops a bit. 5 Reasons You Don’t Need to Learn Machine Learning, 7 Things I Learned during My First Big Project as an ML Engineer, Download the data subset from labrosa Columbia, Convert the data format from h5 to data frame, Scrape songs that have appeared on Top 100 BillBoard chart. Predicting song popularity using track metadata and raw audio features - addt/Song-Popularity DJ Khaled boldly claimed to always know when a song will be a hit. 486 computer with 200 MB hard disk with an AMD K6-2 333 Mhz with 4.3 It included my target variable, a popularity score for each song. The top 10 artists in 2016 generated a combined $362.5 million in revenue. Flexible Data Ingestion. Spoiler alert: my songs did not go far — songs that I was so sure of, that I personally listened to over and over again. Metadata about lyrics that is genre and popularity was obtained from Fell and Sporleder[2]. A script was provided to convert the dataset to mat files to be used with matlab. The new dataset consists of ~30K Flickr images labelled with their engagement scores (i.e., views, comments and favorites) in a period of 30 days from the upload in the social platform. We trained our data on different models to predict if a song is a hit song or not. In 2017, the music industry generated $8.72 billion in the United States alone. • We used a subset of the MSD containing 10,000 songs to train and develop our learning models. Finally, we cleaned the dataset of any invalid entries, and balanced our dataset with an equal amount of 'popular' and 'not popular' songs. The original data in A Million Songs dataset came with a song hotness feature. Thanks to growing streaming services (Spotify, Apple Music, etc) the industry continues to flourish. We can see that for tempo there was a range that hot songs commonly used, and there were two peaks within this range at about 100 bpm and 135 bpm. Using correlation matrix, we can briefly observe which features influences songs’ popularity. After my EDA and running a baseline linear regression model, I applied polynomial transformation to the 2nd degree to all of my song audio features. Want to Be a Data Scientist? The US government’s data portal offers more than 150,000 datasets, and even these are only a fraction of the data resource available through US … Tuning saw the AUC score increase from 0.632 to 0.68. I created my own YouTube algorithm (to stop me wasting time). Track Popularity Dataset. Observing Songs' Popularity Important Features of Popular Songs. My final model wasn’t as predictive as I had hoped, explaining only 28% of the amount of variation in song popularity. Here we can see the f1-scores for each feature in our final dataset. Therefore many fields had to be dropped. Predict which songs a user will listen to. Importing and using the Million Song Dataset in Azure SQL DB or SQL Server (2017+) to build a recommendation service for songs.. Getting Started Prerequisites. We modified the script so that it would produce a csv that we could use to train our models. Familiarity is on the x-axis and ranges from 0 to 1 as well, describing how ‘familiar’ the artist is based on an algorithm by Echo Nest. I thought this feature would impact the popularity score the most. DJ Khaled boldly claimed to always know when a song will be a hit. A value of -1 represents 100% confidence that the key is minor and 1 represents 100% confidence that the key is major. Python: 6 coding hygiene tips that helped me get promoted. Predict which songs a user will listen to. All one million songs came out to about 280 GB. Many fields in the dataset were unusable due to old deprecated data. Artist related features: artist … Again, as shown above, the relationships between each of my features and target variable were largely non-linear. It performed significantly better. Over at Hifi we have found the data from the Million Songs Dataset quite useful in building some of our initial recommendations algorithm prototypes, but to make the data actionable, having it in a simpler format (such as a csv) really simplifies things. • To measure popularity, we used “hotttnesss”, which is a metric After testing out a few different selection methods, such as RFECV,VIF and Lasso. Tempo was at about 122 bpm and had a standard deviation of 33 bpm, artist familiarity was at 61% and had a standard deviation of 16%, most songs were in a major key but the standard deviation was rather wide, loudness was at about -10 dB, and artist hotness was at about 0.43. I was mostly content with all of my possible features, but as an avid Spotify user, I knew that Spotify keeps a follower count for each artist. Every song in the dataset contains 41 features categorized by audio analysis, artist information, and song related features. Take a look. I felt that this could be a great addition to my predictors of song popularity, so I used python to make API requests to the public Spotify API to gather this count for all my of songs. The data is stemmed. Audio analysis features: tempo, duration, mode, loudness, key, time signature, section start, Artist related features: artist familiarlity, artist popularity, artist name, artist location, Song related features: releases, title, year, song hotness. We utilized two large datasets. We've paired each of the 27,000+ songs that have appeared on the Hot 100 with an appropriate genre. Every song has key characteristics including lyrics, duration, artist information, temp, beat, loudness, chord, etc. To determine a genre for each song, we leaned heavily on the Spotify API, with supplemental data from EveryNoise.com, AllMusic.com, and Wikipedia for songs missing from the streaming service. It may have been easier to predict non hit songs because our data was skewed, with only 1,200 hit songs. KPOP JUICE is a site that summarizes various information about KPOP auditions, popular ranking of KPOP idol groups, trends and more. My second model that I ran used all of my original features as well as all of the interaction features created via polynomial transformation. You can see the explanation at the Million Song Dataset home ; If you use the data, please cite both the data here and the Million Song Dataset. In this paper, we have presented “BanglaMusicStylo”, the very first stylometric dataset of Bangla music lyrics. My main points of cleaning were: The next step in my process was to utilize exploratory data analysis and statistical testing to gain further insight into my dataset. Ellis, Brian Whitman, and Paul Lamere. song_hotttnesss the popularity of a song measured with value of between 0 - 1. After getting the list of songs that have been on billboard, we go back to our 10,000 songs dataset, and classified them accordingly. As this value approaches 1, the hotness of the song also approaches 1 (who’d have thought?). XGBoost provided the best predictions on the training model, with an AUC score of 0.68. Audio analysis features: tempo, duration, mode, loudness, key, time signature, section start. A linear regression project using Spotify song data, This project idea recently came to me after participating in a bit of Zoom quarantine fun — a Zoom facilitated music bracket. For my first model, I used one feature that seemed to have the highest correlation with popularity, artist follower count. I started by sourcing a Spotify dataset from Kaggle that contained the data of 2,000 songs. The MSD contains metadata and audio analysis for a million songs that were legally available to The Echo Nest. Predict which songs a user will listen to. The table below shows the results of some of the models that we tried. Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. The following features had the most positive and negative impact on popularity. So, it returns the list of the popular songs for the user but since it is popularity based recommendation system the recommendation for the users will not be affected. I used matplotlib, seaborn and pandas for the EDA. The primary identifier field for all songs in dataset. Its purposes are: To encourage research on algorithms that scale to commercial sizes; To provide a reference dataset for evaluating research; As a shortcut alternative to creating a large dataset with APIs (e.g. Individual h5 files were provided for each song. The dataset used in this challenge is an extension of the Social Popularity Image Dynamics dataset (SPID 2018) used in [1] and [2].. Out of 10,000 songs in our dataset 1192 songs were classified as hot songs. It included my target variable, a popularity score for each song. We can see some interesting trends on the graph above as well. This significantly increased the importance of this value as we’ll see in the next section. First, deploy an Azure SQL database, SQL Server (2017+)here.This sample correctly on both … Exchanging emails with Dianne Cook, we pondered the idea of creating a simplified genre dataset from the Million Song Dataset for teaching purposes.. DISCLAIMER: I think that genre recognition was an oversimplified approximation of automatic tagging, that it was useful for the MIR community as a challenge, but that we should not focus on it any more. Before getting into modeling, my goal was to get a deeper understanding of the relationship between my target and feature variables, as well as a better grasp on how my features related to one another. The script we developed to map Spotify API data to our training data can be viewed here. I also would like to consider other explanatory variables that could be added into my dataset. The y-axis is in terms of the song hotness Y, where 0 is the lowest score and 1 is the highest score. We have collected 2824 Bangla song lyrics of 211 lyricists in a digital form. To address these requirements, we introduce the Track Popularity Dataset (TPD), a collection of track popularity data for the purposes of MIR, containing: 1. fft sources of popularity de nition ranging from 2004 to 2014, 2. information on the remaining, non popular, tracks of an album with a pop-ular track, Random predictions would yield a 0.5 AUC score. Chroma, 84 attributes 2. We were interested in the distribution of hit songs, so we isolated all songs with a hotness value of 1 and graphed the distribution of different features for these songs. We also had to detect and remove duplicate lyrics. An example of this is the artist familiarity field which had only 10 missing values. I'd like a more complete listing with the title, artist and year at the bare minimum. While there wasn’t a ton of information around provenance or methodology, this Chicago Crime Dataset proved to be a very interesting, and robust, dataset to play with. However, around 4500 songs were missing this feature, which is almost half of the subset we were using. As we would expect, the familiarity of the artist has a correlation to the hotness value. Some features that were only missing a reasonable amount we decided to fill in the missing values with the mean. It would be wonderful if there's a database containing every song ever published by major labels, with extra fields like "genre" and when and if they became hits, and how big of a hit, and how long. Every song in the dataset contains 41 features categorized by audio analysis, artist information, and song related features. This project demonstrated the possibility of predicting music hotness, identified trends in popular music, and developed feature extraction tools using Spotify’s API. Project by Mohamed Nasreldin, Stephen Ma, Eric Dailey, Phuc Dang. This is a good question because the Million Song Dataset (MSD) is a great resource, but is also very limited. For example, n_estimators and learning rate were tuned together as a higher n_estimators value required a lower learning rate to produce optimal results. The question of what makes a song popular has been studied before with varying degrees of success. The top 10 artists in 2016 generated a combined $362.5 million in revenue. (2011) The Million Song Dataset. For example: I have a dataset of 100 rows. For statistical testing, I utilized scipy and statsmodels. But as you can see above, it wasn’t very insightful with an R-squared value of .09. The week prior, each participant was tasked with nominating four songs that they felt the group did not know but would enjoy. Dataset; Groups; Activity Stream; Baby Name popularity over time This data set lists the sex and number of birth registrations for each first name, from 1900 onward. Some feature engineering is then done in order to convert the Spotify data back to a format that is usable for our model. Existing datasets do not address the research direction of musical track popularity that has recently received considerate attention. Years are grouped by the date of the birth registration, not by the date of birth. The range of confidences for minor lie between -1 and 0 and the range of confidences for major lie between 0 and 1. The main problem with this dataset was the format provided. Many data fields were missing and there was no echonest API to fill in data since the API was modified by Spotify. We decided to further investigate by asking three key questions: Are there certain characteristics for hit songs, what are the largest influencers on a song’s success, and can old songs even predict the popularity of new songs? We had to do extensive preprocessing to remove text that is not part of lyrics. I trained and tested linear regression models using statsmodels and scikit-learn. Additionally, it might also be worth exploring other types of models that would be better suited to this dataset. A grid search was run on XGBoost to further improve the AUC score. This created interactions among the different song elements, which in hindsight really made sense because it’s the combination of elements that make up a song. Predicting the popularity of news can be formulated in many ways (see Section “Problem Variations”). The first was compiled through the use of a Billboard API.The second was from Kaggle.We utilized the Genius API and Spotify API to scrape a variety of additional text and audio features. Below is a table of online music databases that are largely free of charge.Note that many of the sites provide a specialized service or focus on a particular music genre.Some of these operate as an online music store or purchase referral service in some capacity. In 2012 alone, the U.S. music industry generated $15 billion. In addition, we may consider using the full dataset to see if we can improve our models. [4] We extracted hundreds of features from each song in the dataset, including metadata, audio analysis, string features, and common artist locations, and used various ML methods to determine which of these features were most important in … We decided to predict some new songs using our model. • The Million Song Dataset (MSD) contains almost 500 GB of song data and metadata from which we extract features for our learning models. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! Make learning your daily ritual. We wrote python scripts using BeautifulSoup to scrape billboard.com and get all the songs that appeared on the chart from 1958 to 2012. After testing our model on new songs pulling from Spotify, we observe that it is significantly simpler to correctly predict a bad song rather than a hit. Matthew Lasar - Mar 8, 2011 2:22 pm UTC The Dataset I started by sourcing a Spotify dataset from Kaggle that contained the data of 2,000 songs. Moving forward, we would like to explore how additional features such as artist location or release date can influence a song’s popularity. … The main Dataset. The artist information shows that most of these artists had to have been ‘one-hit wonders’ due to their lack of hotness and familiarity. All participants spent a week listening to the choices and prepped for casting their votes for each matchup of songs. The duration of the hot songs were at about 200 seconds on average and this duration had a general range of 3 to 4 minutes. To cite: Thierry Bertin-Mahieux, Daniel P.W. Our data model has the ability to calculate all the chart statistics that you want » Peak position, debut date, debut position, peak date, exit date, #weeks on chart, weeks at peak plus graphs to visualize a song's week-by-week chart run including re-entries. The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. SONG(iKON)'s Wiki profile, social networking popularity rankings and the latest trends only available here are all available here. The process can be summarized as followed: After collecting the data and cleaning it to be used, we then moved on to data exploration by looking into feature importance, trends in our dataset, and identifying the optimal values for these features. Though there is generally more activity in the regions that also produce hits, we can see that the hits are centralized around these specific areas. Thus we can expect the model to use this to predict whether or not a song is a hit. Below are the results of some other songs that our model has predicted as well as the Spotify hotness results to compare them against: Going into this endeavour, we were uncertain if it is even possible to predict, better than random, if a song will be popular or not. This is a digital catalog of every title to appear on a music popularity chart in the last 80 years organized into a relational database. Most of the activity is coming from the western side of the world, and on North America, we can also see a divide between east coast and west coast. The Million Song Dataset (MSD) is our attempt to help researchers by providing a large-scale dataset. And so my quest to build a prediction model for song popularity began…. It has over 9.5 million Twitter followers and over 6.5 million fans on Facebook. predicting song hotttnesss. Mashable Inc.is a digital media website founded in 2005. Xgboost appears to be the one with the highest accuracy at 0.63 area under the curve (AUC) score, before tuning. The songs are rep-resentative of recent western commercial music. The outline follows these five steps: 1.register on the Kaggle website, 2.acquire the training data, 3.write a Python script that computes song popularity, To answer these questions, we made use of the Million Song Dataset provided by Columbia, Spotify’s API, and machine learning prediction models. In 2017, the music industry generated $8.72 billion in the United States alone. However, after analyzing my coefficients, there were a few takeaways to be noted. Using the Spotify ID audio features and in depth audio analysis can then be grabbed for a song. * The dataset is split into four sizes: small, medium, large, full. Each parameter was tuned, and some values were hypertuned simultaneously. Thankfully there was a randomly selected subset that is only 10,000 songs. 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