In the last article on the blog, we covered how Cyanite’s Similarity Search can be used in music catalogs. In this article, we explore another way to search for songs using Dynamic Keyword Search and how to leverage it to create mood- and contextual-based playlists. 

Rather than relying on a reference track, Dynamic Keyword Search allows you to select and combine from a list of 1,500 keywords and adjust the impact of these keywords on the search. This is especially helpful to create playlists where songs match in mood, activity, or other characteristics. 

But before we explain how this feature works, let’s explore how playlists are created. What makes a perfect playlist? Why are playlists so essential when utilizing a music catalog? And how can the Dynamic Keyword Search help with that?

There are three techniques for playlist creation:

1. Manual creation (individually picking songs) 
2. Automatic generation and recommendation 
3. Assisted playlist creation. 

Historically, manual creation has been the most basic and old approach. It involves picking songs individually for playlists. It might be the simplest technique but the amount of time and effort that goes into it can be overwhelming. Imagine you are working 100,000 audios in a catalog and have to create an “Energetic Workout” and “Beach Party” playlist. 

Automatic generation uses various algorithms to create playlists with no human intervention. One of the most famous ones is, for example, “Discover Weekly” by Spotify. 

Assisted playlist creation uses music technology to guide and support manual playlist creation. 

In the research by Dias, Goncalves, and Fonseca, manual playlist creation was found to be most effective in terms of control, engagement, and trustiness. This means that people trust handmade playlists. Also, manual creation provides the most amount of control over the outcome and it engages editors in the creation process. 

Automatic creation was found to be the most effective in adapting to the listeners’ needs. There is no manual control involved, so automatic tools can adapt and change playlists in no time. 

Assisted techniques were found to be most effective in terms of engagement and trustiness whilst being quick to create. They also performed well on the song selection criteria. Song selection has been defined as the most critical factor in the playlist creation process according to this study. However, while song selection is considered very important, the question of what makes a song right for the particular playlist is still open. Apart from that, assisted techniques proved to be optimal in control, and serendipity and they also can adapt to listening preferences rather easily. 

To anticipate things already: The Dynamic Keyword Search is exactly such an assisted technique in playlist creation.

Why are search tools for playlist creation important in a catalog?

Playlists have been known to be the ultimate tool for promoting music. We already covered the ways artists can get on Spotify and other people’s playlists in other articles on the blog. But creating playlists can also be beneficial for catalog owners and catalog users, be it professional musicians or labels. Here is why: 

• You can realize new and passive modes to exploit and monetize your catalog. If you make it easier for your users and/or customers to explore your catalog, you directly increase its value.
• Playlists are used as a promotional tool to showcase the works of an artist or the inspirations behind the artist. This article recommends creating two playlists: a vibe playlist and a catalog playlist for brand engagement and streams. 
• Playlists help organize music by theme or context. 
• With playlist creation features, users save time on finding the right fitting songs
• Playlists can be indexed separately in search results. This helps music get discovered. 

So playlist creation tools in a catalog are pretty important. Similarity Search is one of these tools. Another one, which we focus on in this article is Dynamic Keyword Search.

Cyanite’s Dynamic Keyword Search allows for searching tracks based on multiple keywords simultaneously where each keyword can be weighted for its impact on the search. This feature leads to more relevant search results with less time-effort spent on search.

Usually, the keywords you choose represent your idea of what you’re searching for. But you don’t have full control over the search. With Dynamic Keyword Search, you can increase the precision of the search results by adjusting the impact of the keywords on the search. So you can express exactly what you’re looking for. There are 1,500 keywords to choose from representing such characteristics of the song as mood, genre, situation, brand values, and style. These keywords’ impact on search can then be adjusted on the scale from -1 to 1 from no impact at all to “heavy impact”.

Cyanite Dynamic Keyword Search interface

Not all playlists are created equal. Some are better than others. This study outlines 5 characteristics of playlists that can indicate a good or bad playlist. The authors of the study assumed that user-generated playlists could be an indicator for the algorithms to create good playlists. Here are the 5 playlist characteristics they outlined: 

• Popularity – most user-generated playlists feature popular tracks first. This, however, is not too obvious though but grabbing the attention spans of the listeners from the start is important. 
• Freshness – playlists should contain recently released tracks. Most playlists in the study contain tracks released on average in the last 5 years.
• Homogeneity and diversity –  playlists on average cover a very limited number of genres so playlists should be rather homogenous. However, diversity plays a significant part in listeners’ satisfaction so it should be incorporated into the playlist as well.
• Musical Features – in terms of energy, playlists with a narrow energy spectrum with a low average energy level are preferred, but there can be some high-energy tracks in the list. 
• Transition and Coherence – the similarity between the tracks defines the smoothness in transition and coherence of the playlist. Usually, user-generated playlists have a better similarity in the first half and a lesser similarity in the second half. 

As the study deals with a variety of user-generated playlists, it can’t be said that all of them were equally good playlists. But the criteria outlined above can help improve playlists by understanding the character of the playlist. With Dynamic Keyword Search, you can control such criteria as homogeneity and diversity, musical features such as energy level, and similarity between the tracks to ensure transition and coherence. 

PRO TIP: To improve a playlist’s transition and coherence you can combine the Dynamic Keyword Search with our Similarity Search to further filter music on Camelot Wheel. The Camelot Wheel indicates which songs transition harmonically well giving you an extremely powerful tool to perfect the song order. You can find a deeper explanation of that in this article.

Here is how to access Dynamic Keyword Search in the Cyanite app. This feature is also available through our API. 

1. Go to Search in the menu and select the Keyword Search tab. Choose whether to display results from the Library or Spotify. 
2. Select keywords from the Augmented Keywords set. For example, these are some of the keywords in the list: joy, travel, summer, motivating, pleasant, happy, energetic, electro, bliss, gladness, auspicious, pleasure, forceful, determined, confident, positive, optimistic, agile, animated, journey, party, driving, kicking, impelling, upbeat. We recommend selecting up to 7 keywords out of 1,500. 
3. Adjust the weights for each keyword from 1 to -1 to define their impact on search. For example, let’s set  the search input as sparkling: 0.5, sad: -1, rock: 1, dreamy: 1 
4. Scroll down for search results. The search results will return tracks from the library that are dreamy, slightly sparkling, and not at all sad. They will also all be rock songs.

Dynamic Keyword Search can be requested from our support team.

There are various ways to create playlists from manual creation to automatic and assisted techniques. An assisted approach that combines automatic and manual creation has proved to be the most effective in playlist creation. It meets almost all the editors’ needs such as providing control over the process, maintaining a high level of engagement and trustworthiness, and offering a good selection of songs. However, the automatic approach is fast developing and algorithms might substitute human work completely in the future. 

Our Dynamic Keyword Search feature can help you create playlists as one of the assisted techniques. It can provide search results that take into account the search intent  in terms of keywords and the impact of those keywords on search. This doesn’t mean that Dynamic Keyword Search replaces the manual work completely, but it can help artists, labels, and catalog owners do the creative work and engage fans and listeners with the support of the right tools to save time, money, and effort. This is what we’re striving to achieve here at Cyanite – to help you fully unlock your catalog’s potential.

Let us know if this article has been helpful and stay tuned for more on the Cyanite blog! 

I want to try Dynamic Keyword Search – how can I get started?

If you want to get the first grip on Cyanite’s technology, you can also register for our free web app to analyze music and try similarity searches without any coding needed.

Sources

From manual to assisted playlist creation: a survey by Ricardo Dias, Daniel Gonc¸alves, Manuel J. Fonseca

Effects of recommendations on the playlist creation behavior of users by Iman Kamehkhosh, G. Bonnin, D. Jannach

Other articles in the series include: 

How to Turn Music Data into Actionable Insights: This is an overview of the Data Pyramid and how it can be used in the music industry. 

An Overview of Data in The Music Industry: This article gives a list of all types of metadata in the music industry.

Making Sense of Music Data – Data Visualizations: This article explores data visualizations as the second step of the pyramid and gives examples of visualizations in the music industry. 

Benchmarking in the Music Industry – Knowledge Layer of the Data Pyramid: This article deals with Knowledge and how it is used to benchmark performance and set expectations.

Intelligence can be described as the ability to choose one decision out of a million other decisions based on knowledge of how these decisions might affect the outcome. 

Intelligence can be generated by the machine, for example, a self-driving car is a form of intelligence that scans the environment and can predict the course of action for the next section of the road. In the music industry, intelligent decisions are still, for the most part, made by humans by examining information, reading graphs and charts, memorizing past outcomes, and monitoring real-time data. In this article, we’ll explore some of the emerging intelligence technology in the music field so keep reading to find out more.

1. Recommendation systems that learn and adapt effectively to individual users’ preferences

Recommendation systems already use some sort of prescriptive analytics when they make a selection of songs based on past user behavior. Recommendation systems can also take into account the sequence of songs and context that affect the enjoyment level of the playlist as a whole. As previously played songs influence the perception of the next song, the playlist can be adjusted accordingly. The ability to prescribe a listening experience by recommendation systems is, perhaps, the most common and well-developed example of intelligence in the music industry.

Additionally, recommendation systems can prescribe music that directly affects user behavior. This project, for example, uses data from running exercises, predicts the future running performance, and recommends songs that maximize running results. It does so continuously, as the system stores and learns from each updated running exercise record.

To learn more about different types of recommendation systems, check out the article How Do AI Music Recommendation Systems Work. 

2. Automatic playlist generation based on context

Generating music or suggesting existing music based on the context is an analog of a self-driving car in the music industry. The music adapts to the listening situation to amplify the current experience. For instance in video games, where music adjusts to the plot as the user progresses through various levels of the game. More on that in our article on Omniphony engine that explores adaptive soundtracks and music context in game development.

Such systems are also used as location-aware music recommendations for travel destinations (when music is chosen based on the sightseeing place you visit), or computer vision systems for museum experiences (when the artwork dictates the audio choice). In these cases, the constantly changing environment serves as the basis for recommendations. 

Another example of intelligence in this field is generating music in the metaverse which is a virtual environment, that includes augmented reality. The metaverse can be accessed through Oculus headsets and a smartphone. Currently, virtual streams and concerts are already conducted in the metaverse, so it is only a matter of time till the curated immersive experiences that can adjust to the audience’s needs will be delivered using intelligence.

3. Prescriptive curatorship – What’s going to be hot next? 

Prescriptive curatorship entails an understanding of how up-and-coming artists and tracks will perform and who is more likely to break in the near future. In the past, platforms like Hype Machine indexed music sites and helped find the best new music. 

Nowadays, there are systems that can predict future hits and breaking artists automatically. For example, Spotify is developing algorithms that can predict future-breaking artists. The algorithm takes into account the preferences of the early adopters and then determines whether the artist can be considered breaking. This data can then be used to sign deals with the artist at a very early stage.

4. Tracking changes in music preference distribution  – making music that hits the current preferences or even future preferences

Unlike prescriptive curatorship that relies on a group of experts, music preference distribution numbers serve artists to show how their chosen genre and formats fit audience demographics and how music can be changed for current or future preferences. The general consensus in the music industry is that music preference algorithms come after the music is produced, otherwise all music will end up sounding the same to mimic popular artists. 

There is not yet a system that would automatically recommend changing the content of the song based on what users prefer. Nevertheless, attempts to use the numbers to create songs people will like are still being made.

5. Royalty Advances

Royalty advances are a complex task that requires comprehensive tracking of music consumption across all platforms. Distributors such as Amuse and iGroove offer a royalty advance service that is able to predict upcoming payout amounts so that artists can invest in their music long before the actual royalties kick in. These systems analyze streaming data to calculate upcoming earnings. 

Recently the topic got even more attention through the hype of NFTs. Crypto-investors want to predict future royalty payouts and the value of their asset. 

Future platforms most likely will be able to prescribe a course of action regarding which distribution platform to focus on based on the predicted royalty amounts. 

The main concern in the industry is how far can one go with technological intelligence considering that music is a creative activity and the human element is still largely prevalent. An intelligence machine that can tell which music to produce based on a prediction of future user preferences generally prompts an adverse reaction in the industry. 

Nevertheless, intelligent decisions to adjust the content of songs or to sign future-breaking artists identified by the AI can already be made by the artists and labels based on available data. 

At Cyanite, we provide our API for access to data and the development of any kind of intelligence engines. As always, at each level of the pyramid, the quality of data plays a vital role. Cyanite generates data about each music track such as bpm, dominant key, predominant voice gender, voice presence profile, genre, mood, energy level, emotional profile, energy dynamics, emotional dynamics, instruments, and more.

I want to analyze my music data with Cyanite – how can I get started?

If you want to get the first grip on Cyanite’s technology, you can also register for our free web app to analyze music and try similarity searches without any coding needed.