This project demonstrated the possibility of predicting music hotness, identified trends in popular music, and developed feature extraction tools using Spotify’s API. I used matplotlib, seaborn and pandas for the EDA. Mode is whether the song uses a major or a minor key in its production. DJ Khaled boldly claimed to always know when a song will be a hit. Therefore, each lyricist have their own dictionary of thoughts to put on music lyrics. Hi. 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 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, 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 … KPOP JUICE is a site that summarizes various information about KPOP auditions, popular ranking of KPOP idol groups, trends and more. This is a good question because the Million Song Dataset (MSD) is a great resource, but is also very limited. * 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. For my first model, I used one feature that seemed to have the highest correlation with popularity, artist follower count. Matthew Lasar - Mar 8, 2011 2:22 pm UTC We also had to detect and remove duplicate lyrics. 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. Therefore many fields had to be dropped. Popular songs secure the lion’s share of revenue. The range of confidences for minor lie between -1 and 0 and the range of confidences for major lie between 0 and 1. However, after analyzing my coefficients, there were a few takeaways to be noted. After getting the list of songs that have been on billboard, we go back to our 10,000 songs dataset, and classified them accordingly. We utilized two large datasets. It may have been easier to predict non hit songs because our data was skewed, with only 1,200 hit songs. I began to suspect that I would need to transform my variables and create interactions to deal with the non-linear relationships and low correlations. 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 significantly increased the importance of this value as we’ll see in the next section. My final model wasn’t as predictive as I had hoped, explaining only 28% of the amount of variation in song popularity. Moving forward, we would like to explore how additional features such as artist location or release date can influence a song’s popularity. The top 10 artists in 2016 generated a combined $362.5 million in revenue. Finally, we cleaned the dataset of any invalid entries, and balanced our dataset with an equal amount of 'popular' and 'not popular' songs. However, around 4500 songs were missing this feature, which is almost half of the subset we were using. It included my target variable, a popularity score for each song. • To measure popularity, we used “hotttnesss”, which is a metric The top 10 artists in 2016 generated a combined $362.5 million in revenue. Ellis, Brian Whitman, and Paul Lamere. I created my own YouTube algorithm (to stop me wasting time). 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, Does lyric complexity impact song popularity, and can analysis of the Billboard Top 100 from 1955–2015 be used to evaluate this hypothesis? song_hotttnesss the popularity of a song measured with value of between 0 - 1. DJ Khaled boldly claimed to always know when a song will be a hit. And so my quest to build a prediction model for song popularity began…. Using correlation matrix, we can briefly observe which features influences songs’ popularity. I thought this feature would impact the popularity score the most. considered lyrics to predict a song’s popularity, Python Alone Won’t Get You a Data Science Job. The week prior, each participant was tasked with nominating four songs that they felt the group did not know but would enjoy. First a search is run using the search endpoint on the API in order to grab the Spotify ID. We chose this dataset for its large amount of features and size. We can see some interesting trends on the graph above as well. The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. A song is never just one audio feature. 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. 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. 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. My failed choices left me seeking to understand if song popularity can be predicted and what that looks like. Many data fields were missing and there was no echonest API to fill in data since the API was modified by Spotify. 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. A value of -1 represents 100% confidence that the key is minor and 1 represents 100% confidence that the key is major. Some features that were only missing a reasonable amount we decided to fill in the missing values with the mean. Biz & IT — Million-song dataset: take it, it’s free A dataset of the characteristics of one million commercially available songs …. 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. 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. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. 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. 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. However, with the proportion of 85 features to my dataset of 2,000 — I knew that I needed to cut down my features and only include those that really had an impact to avoid multi-collinearity and overfitting. The original data in A Million Songs dataset came with a song hotness feature. I started by sourcing a Spotify dataset from Kaggle that contained the data of 2,000 songs. predicting song hotttnesss. Predicting song popularity using track metadata and raw audio features - addt/Song-Popularity The y-axis is in terms of the song hotness Y, where 0 is the lowest score and 1 is the highest score. 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. We've paired each of the 27,000+ songs that have appeared on the Hot 100 with an appropriate genre. • The Million Song Dataset (MSD) contains almost 500 GB of song data and metadata from which we extract features for our learning models. The question of what makes a song popular has been studied before with varying degrees of success. Every artist in the data was uniquely identified by a string, so we decided to do label encoding on them. Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. 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. As this value approaches 1, the hotness of the song also approaches 1 (who’d have thought?). The data is stemmed. • We used a subset of the MSD containing 10,000 songs to train and develop our learning models. Since Spotify acquired EchoNest, many different features were changed including a simple way to look up song info by ID. Spoiler alert: my songs did not go far — songs that I was so sure of, that I personally listened to over and over again. 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. Another alternative is to use Spotify API to collect our own data. Thankfully there was a randomly selected subset that is only 10,000 songs. Regression Formulation:Given the features of an article, predict the “number of shares” that the article will get once it is published. The script we developed to map Spotify API data to our training data can be viewed here. 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. I trained and tested linear regression models using statsmodels and scikit-learn. The dataset was too large as well. 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. Additionally, it might also be worth exploring other types of models that would be better suited to this dataset. XGBoost provided the best predictions on the training model, with an AUC score of 0.68. The dataset used in this challenge is an extension of the Social Popularity Image Dynamics dataset (SPID 2018) used in [1] and [2].. Predict which songs a user will listen to. To answer these questions, we made use of the Million Song Dataset provided by Columbia, Spotify’s API, and machine learning prediction models. With these two values, we combined the features to range from -1 for minor to 1 for major. The Dataset I started by sourcing a Spotify dataset from Kaggle that contained the data of 2,000 songs. It has over 9.5 million Twitter followers and over 6.5 million fans on Facebook. The artist information shows that most of these artists had to have been ‘one-hit wonders’ due to their lack of hotness and familiarity. Chroma, 84 attributes 2. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! The table below shows the results of some of the models that we tried. We had to do extensive preprocessing to remove text that is not part of lyrics. SONG(iKON)'s Wiki profile, social networking popularity rankings and the latest trends only available here are all available here. Random predictions would yield a 0.5 AUC score. First, deploy an Azure SQL database, SQL Server (2017+)here.This sample correctly on both … 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. All one million songs came out to about 280 GB. Again, as shown above, the relationships between each of my features and target variable were largely non-linear. Predicting the popularity of news can be formulated in many ways (see Section “Problem Variations”). In addition, we may consider using the full dataset to see if we can improve our models. But I want to split that as rows. 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. For the songs that made Billboard’s Top 100, we were looked into average and standard deviation for some top features we detected previously using f1-score and the results were fairly reasonable. Using the Spotify ID audio features and in depth audio analysis can then be grabbed for a song. Observing Songs' Popularity Important Features of Popular Songs. We stopped at 2012 since to our most recent songs in the dataset were released in 2012. Artist related features: artist … In 2012 alone, the U.S. music industry generated $15 billion. But as you can see above, it wasn’t very insightful with an R-squared value of .09. 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. A grid search was run on XGBoost to further improve the AUC score. Every song in the dataset contains 41 features categorized by audio analysis, artist information, and song related features. The music industry has undergone a dramatic change. 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. It performed significantly better. I'd like a more complete listing with the title, artist and year at the bare minimum. It included my target variable, a popularity score for each song. Want to Be a Data Scientist? Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. The main Tuning saw the AUC score increase from 0.632 to 0.68. I also would like to consider other explanatory variables that could be added into my dataset. Project by Mohamed Nasreldin, Stephen Ma, Eric Dailey, Phuc Dang. My second model that I ran used all of my original features as well as all of the interaction features created via polynomial transformation. 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. For example: I have a dataset of 100 rows. The primary identifier field for all songs in dataset. Thanks to growing streaming services (Spotify, Apple Music, etc) the industry continues to flourish. 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 present a model that can predict how likely a song will be a hit, defined by making it on Billboard’s Top 100, with over 68% accuracy. We modified the script so that it would produce a csv that we could use to train our models. Python: 6 coding hygiene tips that helped me get promoted. Having a fundamental understanding of what makes a song popular has major implications to businesses that thrive on popular music, namely radio stations, record labels, and digital and physical music market places. 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’. Music Information Research requires access to real musical content in order to test efficiency and effectiveness of its methods as well as to compare developed methodologies on common data. The MSD contains metadata and audio analysis for a million songs that were legally available to The Echo Nest. The dataset chosen was the Million Songs Dataset provided by Columbia University and pulled from Echo Nest. All participants spent a week listening to the choices and prepped for casting their votes for each matchup of songs. To cite: Thierry Bertin-Mahieux, Daniel P.W. Thus, we wanted to find a new way to classify if a song is a hit or not. I merged my two datasets on artist name and began the process to clean the data for modeling using pandas. Take a look. Our dataset contains around 400,000 songs in English. 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. Predict which songs a user will listen to. I want to split dataset into train and test data. Many fields in the dataset were unusable due to old deprecated data. The demo below shows our script in action. Metadata about lyrics that is genre and popularity was obtained from Fell and Sporleder[2]. 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. After testing out a few different selection methods, such as RFECV,VIF and Lasso. As we would expect, the familiarity of the artist has a correlation to the hotness value. … Flexible Data Ingestion. 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. We trained our data on different models to predict if a song is a hit song or not. For statistical testing, I utilized scipy and statsmodels. Because of this the demo uses a very roundabout way to grab song info. 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. 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? Thus we can expect the model to use this to predict whether or not a song is a hit. In this paper, we have presented “BanglaMusicStylo”, the very first stylometric dataset of Bangla music lyrics. 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. As you can see from the above heat map, my correlations were pretty low across the board and in every direction. Every song has key characteristics including lyrics, duration, artist information, temp, beat, loudness, chord, etc. An interesting trend we can see here is that the actual music aspects of the song are reasonably entangled with artist information. 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. Every song in the dataset contains 41 features categorized by audio analysis, artist information, and song related features. Out of 10,000 songs in our dataset 1192 songs were classified as hot songs. 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. (2011) The Million Song Dataset. Predicting how popular a song will be is no easy task. It also included the bulk of my explanatory variables — audio features such as BPM, valence, loudness and danceability as well as more general characteristics such as genre, title, artist and year released. All in all this was a fun and somewhat insight project. 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. An example of this is the artist familiarity field which had only 10 missing values. The songs are rep-resentative of recent western commercial music. We decided to predict some new songs using our model. 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. Xgboost appears to be the one with the highest accuracy at 0.63 area under the curve (AUC) score, before tuning. Make learning your daily ritual. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Since we spent a significant amount of time in our classroom learning different … * The dataset is split into four sizes: small, medium, large, full. Predict which songs a user will listen to. We wrote python scripts using BeautifulSoup to scrape billboard.com and get all the songs that appeared on the chart from 1958 to 2012. A script was provided to convert the dataset to mat files to be used with matlab. Chicago Crime Dataset. Though this value is straightforward with a 0 for minor and a 1 for major, there was also a value named mode_confidence that depicted the probability of the mode selected being accurate. 486 computer with 200 MB hard disk with an AMD K6-2 333 Mhz with 4.3 The technical features such as tempo, mode, and loudness are about as important as information on the artist such as familiarity, hotness, and identification. In 2017, the music industry generated $8.72 billion in the United States alone. Audio analysis features: tempo, duration, mode, loudness, key, time signature, section start. Years are grouped by the date of the birth registration, not by the date of birth. We have collected 2824 Bangla song lyrics of 211 lyricists in a digital form. 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. [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 … 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. Some feature engineering is then done in order to convert the Spotify data back to a format that is usable for our model. 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. The “mashable” dataset in its raw form makes it a regression problem i.e. 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. 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 following features had the most positive and negative impact on popularity. 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. Each parameter was tuned, and some values were hypertuned simultaneously. Don’t Start With Machine Learning. To increase the predictive power of my model, I would like to try further degrees of polynomial transformations to find better interactions. Mashable Inc.is a digital media website founded in 2005. techniques and the One Million Song Dataset. 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. If a song has appeared on Top 100 BillBoard at least once, then it will be classified as a hit song. Track Popularity Dataset. We decided to use BillBoard Top 100 to determine popularity. 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. Individual h5 files were provided for each song. The two features artist ID and mode were altered to be a better reflection of their properties in the dataset. In 2017, the music industry generated $8.72 billion in the United States alone. Existing datasets do not address the research direction of musical track popularity that has recently received considerate attention. This has been asked a few times before but never answered properly. Previous studies that considered lyrics to predict a song’s popularity had limited success. Dataset. The Million Song Dataset Challenge Getting Started By the end of this document, you should be ready to make a first submission in the Million Song Dataset Challenge on Kaggle. Song or not this has been studied before with varying song popularity dataset of success two features artist and. Did not know but would enjoy to growing streaming services ( Spotify, Apple music, etc the... ” ) hypertuned simultaneously there were a few different selection methods, such as RFECV VIF! -1 for minor lie between -1 and 0 and the latest trends only available here 8, 2011 pm... The research direction of musical track popularity that has recently received considerate attention of! 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2020 song popularity dataset