Assistant Professor at Singapore University of Technology and Design, where she runs a lab on AI for music and audio. 1. A 13-dimensional vector which captures the tone colour for each segment of a song. There are many providers of such services out there, and although they can be accurate their primary drawback is their speed (or lack thereof) — they require trends to emerge before forecasts can be made. Pachet, F., & Roy, P. (2008, September). Give them an edge by adding a little science into the mix? Not opera divas or rock gods, perhaps, but most of us can whistle or hum along to our favourite tunes, sing happy birthday, drum on the steering wheel as we drive, and so on. As can be expected, the latter are more efficient, but the former give us insight into why a song can be considered a hit. More specifically, I’m going to demonstrate how commercial performance of new music can be predicted by quantifying how engaging listeners are likely to find it. Instead of 10-fold cross validation, we also used a test set of chronologically ‘new’ songs. To demonstrate the effectiveness of our algorithm, we decided to take a bold step and begin posting commercial performance predictions for a small selection of brand new songs online — on the day they’re released. This can be made from the objective properties of hit songs and non-hit songs from the pest. Part 3: Predicting Hit Songs by Modelling the Musical Experience — What’s in it for Labels? Hyperlive has allegedly developed an algorithm that predicts a song’s hit potential — simply by using its ‘audio signature’. Part 3: Predicting Hit Songs by Modelling the Musical Experience — What’s in it for Labels? I’ve selected a sample song. How does a label find the best songs to release? A technology proposing to exploit Hit Song Science was introduced in 2003 by an artificial intelligence company out of Barcelona, Spain, called Polyphonic HMI. Since D3 has the smallest ‘split’ between hits and non-hits this result makes sense. Not too shabby. For an easy to read description of these techniques, please refer to Herremans et al. to identify which songs and artists are taking off, and where. So, the term ‘hit-song’ has to be defined. While there is no shortage of hit-lists, it is quite another thing to find non-hit lists. They include average, variance, min, max, range, and 80 percentile of ~1s segments. The rate between the number of saves to the total listeners who streamed the song plays a big role in Spotify’s algorithm. We call this initiative our Friday Forecast. What was a hit ten years ago, is not necessarily a hit song today. My algorithm basically calls get_random with the reduced set of music sets to find out the next song to play. In order to be able to do hit prediction, we first need a dataset of hit / non-hit songs. Except that means hit songs have become increasingly predictable, offering up all their pleasures in the opening half-minute. It’s now been more than 4 months since we began this project, which means we have 3 full months of comparison data. It helps to determine not only how often it should recommend your song, but where and to whom. In order to train the prediction algorithm it is important that there is a test set. Seasoned industry professionals who know a hit song when they hear it. According to one music tech startup, its new technology may have. Looking solely at audio features, Herremans et al. Take a look. So what if one could tip A&Rs hand? Would you believe them? Standard audio features:These included Duration, Tempo, Time signature, Mode (major (1) or minor (0)), Key, Loudness, Danceability (Calculated by The Echo Nest, based on beat strength, tempo stability, overall tempo, and more), Energy (Calculated by The Echo Nest, based on loudness and segment durations). 214–227). This would create a new competitive edge for artists, labels, publishers and platforms, and allow anyone to maximise return on their musical investment. They feel humans are inferior to computers and artificial intelligence in determining what songs you should play. The table below shows the amount of hits collected. How does a songwriter know when they have a hit on their hands? Howard Murphy, founder of Ostereo, believes that algorithms may be encouraging artists to record shorter songs: ‘We’re seeing two trends emerge simultaneously here: the average hit song is getting shorter, while longer songs are becoming hits less often. Algorithm Al (song with lyrics) - YouTube. Thanks for reading, stay safe, and see you next time. That is to say, there will be energy spread across the spectrum and no obviously dominant frequencies. In particular Timbre 3 (third dimension of PCA timbre vector), which reflects the emphasis of the attack (sharpness), seems influential in order to predict hit songs. Copy link. A song is a piece of music. The DiscoRank algorithm, engineered by Amelie Anglade, is what SoundCloud.com uses to aggregate music on its network. By using a machine-learning algorithm, the team could mine official UK top 40 singles charts over the past 50 years to see how important these 23 features are to producing a hit song. Hit Song Science Is Not Yet a Science. And it does so for all 7.8 billion of us. This set is called the root set and can be obtained by taking the top pages returned by a text-based search algorithm. By ‘algorithmic approaches’ I mean using computational methods to analyse large numbers of tracks to help figure out which of them will the biggest hits. So why would anyone believe we can? What if one could objectively analyse the characteristics of a track and quantify its hit potential before it’s released? When we visualise our features over time, this becomes apparent: Interestingly, we see that dance hit songs have become shorter, louder, and according to the Echo Nest ‘danceability’ features, less danceable! For artists, labels, publishers and platforms, that means creating and distributing the most engaging content possible to create the most engaging experiences possible. The aim is to create value for artists, labels, publishers and platforms by giving traditional A&R a science-driven assist. Curiously, Boer notes that Hitwizard is … Wondering what is the best way to solve this problem: Random play a song from a list of given songs in such a way that no songs is repeated until all the songs are played. An alternative — one might say ideal — solution is to figure out which songs are most likely to engage audiences before they’re released. Every week, we work with artists, labels and rights-holders to help them release only the most engaging songs to their audience. Research on this topic is very limited, for a more complete literature overview, please see Herremans et al. In Parts 2 and 3 to be published over the coming weeks, I’ll dive deeper into the data, perform a little math, and calculate the real-world impact of this kind of approach on an artist’s longterm performance and a label’s bottom line. 2. Part 1: Predicting Hit Songs by Modelling the Musical Experience — Proving it’s Possible. This allows the AI to predict what chances a song has of becoming a hit with an accuracy ratio of approximately 66 percent. But it’s not necessary to reliably predict hit songs. For example, its Explore page and search results. Shuzou, China [preprint link], Herremans D., Lauwers W.. 2017. Sony is making an artificial-intelligence algorithm that writes perfect, hit-making songs — Quartz Skip to navigation Skip to content In the first half of the nineties and from the … HITS-Algorithmus Als Hubs und Authorities lassen sich in der Netzwerktheorie herausragende Knoten anhand ihrer Verlinkung einteilen. Drum sound recognition algorithms. The algorithm predicted a 65 percent or higher probability of a hit for all of the top 10, and over 70 percent probability for 6 out of 10 songs. Every week, we select 5 brand new songs for analysis. Therefore, we decided to classify between high and low ranked songson the hit listings. Here's some of the things we noticed: Around 1980 Seems a Creative Period of Pop Music The prediction accuracy of our hit potential equation varies over time. Not too shabby. Prof. Dr. Dorien Herremans — dorienherremans.com, Herremans, D., Martens, D., & Sörensen, K. (2014). INTRODUCTION Is a hit song just an algorithm? But What is clear is that the field of research isn’t going anywhere, especially as music AI advances. While there is no shortage of hit-lists, it is quite another thing to find non -hit lists. Polyphonic HMI has since spun off a new Delaware C corporation, Music Intelligence Solutions, Inc., which used to run uPlaya, a site geared toward music professionals. For a more complete visualisation of features over time, check out my short paper on visualising hit songs: (Herremans & Lauwers, 2017) and accompanying webpage. The Friday Forecast is our attempt to showcase the algorithm we’ve built that can analyse the musical content of a song, quantify how engaging listeners are likely to find it, and predict how commercially successful it’s likely to be. We call these songs 'Expected Hits', since we correctly predicted these songs to be successful. In addition, in follow up research, I looked at the influence of social networks on hit prediction, which also has a significant impact (Herremans & Bergmans, 2017). This resulted in a further performance increase: It’s intriguing that the model predicts better for newer songs. The problem is that gut feeling and self-reported preferences really aren’t reliable predictors of a song’s commercial performance. All features were standardized before training. In order to be able to do hit prediction, we first need a dataset of hit / non-hit songs. The 18th International Society for Music Information Retrieval Conference (ISMIR) — Late Breaking Demo. In this 3-part post, I’m going talk about algorithmic approaches to hit song prediction. Resources can then be put behind the emerging ‘hits’ to bolster them further. EchoNest Analyzer Documentation, URL developer.echonest.com/docs/v4/_static/AnalyzeDocumentation. In this article, I’m going to show you how one might do exactly this by using an experience-driven algorithmic approach to maximise both listener engagement and one’s bottom line. Here we’ll deal with it as an array of floats that represent the waveform of that music measured at 44,100 hz. Visualizing the evolution of alternative hit charts. 355–360). If you want to use accuracy, it should be class specific. By signing up, you will create a Medium account if you don’t already have one. When Holland’s Duncan Laurence won the Eurovision Song Contest in 2019, amid her euphoria Van Dijk pondered whether AI could be harnessed to lock in more hit songs for the country. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss. In International Workshop on Adaptive Multimedia Retrieval (pp. No need to measure (social) media buzz, post-release listening behaviour, playlist adds, marketing spend, nothing. This nifty API allows us to get a number of audio features, based only on the artist name and song title. The features set we looked at in this research is limited, so by expanding this using both low and high level musical features, higher accuracies may be achieved. Ask Question Asked 7 years, 9 months ago. As Jeremy Erlich, Spotify’s co-head of music recently told Billboard, there’s currently a lack of data around unreleased music. We experimented a bit to see which split would work best, as shown in Table 1, this resulted in three datasets (D1, D2, and D3): Each with slightly unbalanced class distribution: The hit listings were collected from two sources: Billboard (BB) and the Original Charts Company (OCC). Before going into any results, I should stress that it makes no sense to use a general classification ‘accuracy’ here, because the classes are not balanced (see Figure 1). We decided that the effectiveness of the model could be optimized by focusing on one specific genre: dance music. This was done for the following features: Timbre — PCA basis vector (13 dimensions) of the tone colour of the audio. 4,000 hit and non-hit songs and extracted each songs audio features from the Spotify Web API. Journal of New Music Research, 43(3), 291–302. (2014). Springer, Cham. Capturing the temporal domain in echonest features for improved classification effectiveness. And here are our latest predictions. This was intriguing to me, and caused me to explore if we could in fact predict hit songs. we don’t actually listen to the music we say we want to listen to, gut feeling and self-reported preferences really aren’t reliable predictors of a song’s commercial performance. Tap to unmute. So what did we extract: 1. USF Student Kai Middlebrook Develops a Machine Learning Algorithm to Predict Hit Songs What’s more, since most of these services have now been acquired by one of the major music companies, any competitive edge they might have offered has been lost. Most probably yes! We give details of some songs which we correctly predicted to be hits here, and give some insight into the features of the song which helped them climb the UK charts. Let’s explore how we can successfully build a hit song classifier using only audio features, as described in my publication (Herremans et al., 2014). Welcome to part 2 of this 3-part introduction to an algorithmic approach to hit song prediction. The most successful algorithms were Logistic Regression and a Neural Network with one hidden layer. Share. This, of course, is the domain of traditional A&R. Indeed, we recently posted a summary of our performance so far. Hits, however, are identified correctly 68% of the time. Again Timbre 3 is present. Algorithm Al (song with lyrics) Watch later. Future research should look into the intriguing evolution of music preferences over time. Two types of models are explored: comprehensible ones and black-box models. Imagine someone came to you and said they could predict your future. Music is therefore one of the very few human universals, which puts it on the same level as food and sex.". Vereinfacht gesagt sind Hubs und Authorities dabei Knoten, die mit vielen anderen Knoten verbunden sind – beispielsweise bekannte Persönlichkeiten in sozialen Netzwerken und Linkverzeichnisse im World Wide Web . In Part 3 I’m going to demonstrate how — by increasing artist performance across the board — a major label could add an extra Billion dollars or more to its bottom line while holding its current number of releases and artist roster constant. Schindler, A., & Rauber, A. Great! (2014). What, then, if one could access this pre-release data? The Echo Nest was bought by Spotify and is now integrated in Spotify API. Looking at the ROC curve below, we see that the model outperforms a random oracle (diagonal line). Of course you wouldn’t. One month later, we compare our predictions with actual performance and evaluate our accuracy. Can we do even better? This makes the tree small and comprehensible, but gives is a low AUC of 0.54 on D1. We run them through our algorithm and predict how big a hit they’ll be for their lead artist. And I’m going to do so with reference to publicly available prediction data. If playback doesn't begin shortly, try restarting your … Details of the classification accuracy can be seen by looking at the confusion matrix, which reveals that correctly identifying non-hit songs is not easy! get_random uses power of 2 to divide the list and then further sub-dividing. Apparently some people think so. [preprint link], Herremans, D., & Bergmans, T. (2017). In order to fit the decision tree on a page, I’ve set the pruning to high. In 2006 however one of the company's founders, ... Percussion instrument signals tend to look a lot like noise - at least at the point where the instrument is hit. Note that songs stay in the charts for multiple weeks, so the amount of unique songs is much smaller: Now that we have a list of songs, we need the audio features that go along with them. Can an algorithm predict hit songs? We were able to predict the Billboard success of a song with approximately 75% accuracy on the validation set, using five machine-learning algorithms. Algorithms (or, at the very least, formal sets of rules) have been used to compose music for centuries; the procedures used to plot voice-leading in Western counterpoint, for example, can often be reduced to algorithmic determinacy. And I’m going to do so with reference to the Hyperlive Friday Forecast. Overall, logistic regression performs best. And now more than ever, as Corson observes, to keep people engaged on every level, we need the very best music the industry can offer. It’s a source of hope, strength, and connection.”. Your home for data science. Specifically, in Part 2 I’m going to show you — via the Friday Forecast — how Hyperlive can boost an artist’s longterm commercial performance — the number of streams or sales they can amass — by a third or more. Well, the more catchy it is, at least. This might be defined as a song that has been in the (dutch) top40. While our customers appreciate how accurate we can be, believe me when I say it’s initially a tough sell. I’ve written elsewhere about the power of music to connect people, to bring them together. We experimented a bit to see which split would work best, as shown in Table 1, this resulted in three datasets (D1, D2, and D3): Each with slightly unbalanced class distribution: The hit listings were collected from two sources: Billboard (BB) and the Original Charts Co… This mean they must be important. Testing that recipe against the mathematical equation for success, and ultimately, using an algorithm to generate hit songs, are logical next steps for the hit … Part 2: Predicting Hit Songs by Modelling the Musical Experience — What’s in it for Artists? And they all rely on data that becomes available only after a song is released.