Hello there, and welcome to this text! I’m going to clarify how I constructed BeatBuddy, an online app that analyzes what you’re listening to on Spotify. Impressed by Spotify Wrapped, it goals to interpret your present temper and supply suggestions which you can tweak primarily based on that evaluation.
When you don’t wish to learn all the things and simply wish to give it a strive, you are able to do so right here: BeatBuddy. For the remaining, maintain studying!
The Delivery of the Undertaking
I’m an information analyst and a music lover, and I imagine that knowledge evaluation is a strong technique to perceive the world we stay in and who we’re as people.
Music, particularly, can act as a mirror, reflecting your id and feelings at a given second. The kind of music you select usually depends upon your present actions and temper. For instance, should you’re figuring out, you would possibly select an brisk playlist to encourage you.
However, in case you are busy finding out or specializing in crushing some knowledge, it’s possible you’ll wish to take heed to calm and peaceable music. I’ve even heard of individuals listening to white noise to focus, which may be described because the sound you hear once you open the home windows of your automotive on the freeway.
One other instance of how music can replicate your temper is at a celebration. Think about you might be having a celebration with pals and you must select the music. If it’s an informal dinner, you would possibly wish to play some easy jazz or mellow tunes. However should you’re aiming for the form of social gathering the place everybody finally ends up dancing on the furnishings or doing their greatest drunken karaoke efficiency of an ’80s hit, you’ll wish to select songs which might be energetic and danceable. We’ll come again to those ideas in a second.
In reality, all of the music you take heed to and the alternatives you make can reveal fascinating points of your persona and emotional state at any given second. These days, folks are inclined to get pleasure from analytics about themselves, and it’s turning into a worldwide pattern! This pattern is called the “quantified self,” a motion the place folks use analytics to trace their actions, corresponding to health, sleep, and productiveness, to make knowledgeable selections (or not).
Don’t get me unsuitable, as an information nerd, I like all these items, however typically it goes too far — like with AI-connected toothbrushes. Firstly, I don’t want a toothbrush with a Wi-Fi antenna. Secondly, I don’t want a line chart exhibiting the evolution of how properly I’ve been brushing during the last six weeks.
Anyway, again to the music trade. Spotify was one of many pioneers in turning person knowledge assortment into one thing cool, and so they referred to as it Spotify Wrapped.
On the finish of the 12 months, Spotify compiles what you’ve listened to and creates Spotify Wrapped, which matches viral on social media. Its recognition lies in its capability to disclose points of your persona and preferences which you can examine to your folks.
This idea of how Spotify collects and aggregates knowledge for these year-end summaries has at all times fascinated me. I bear in mind asking myself, “How do they do this?” and that curiosity was the place to begin for this undertaking.
Properly, not precisely. Let’s be sincere: The thought to research Spotify knowledge was written on a observe titled “knowledge undertaking”-you know, the form of observe full of concepts you’ll most likely by no means begin or end. It sat there for a 12 months.
Someday, I regarded on the checklist once more, and with a brand new confidence in my knowledge evaluation abilities (because of a 12 months of development and enhancements of ChatGPT), I made a decision to select an merchandise and begin the undertaking.
At first, I simply wished to entry and analyze my Spotify knowledge for no specific goal. I used to be merely curious to see what I may do with it.
Beginning a undertaking like this, the primary query you wish to ask your self is the place the information supply is and what knowledge is accessible. Basically, there are two methods to acquire your knowledge:
- Within the privateness settings, you’ll be able to request a duplicate of your historic knowledge, however it takes 30 days to be delivered — probably not handy.
- Utilizing Spotify’s API, which lets you retrieve your individual knowledge on demand and use completely different parameters to tweak the API name and retrieve varied info.
Clearly, I went for the second possibility. To take action, you first must create a developer undertaking to get your API keys, and then you definately’re good to go.
API Response Instance
Bear in mind we talked about the truth that sure tracks are extra probably danceable than others. As human beings, it’s fairly straightforward to really feel if a track is danceable or not — it’s all about what you’re feeling in your physique, proper? However how do computer systems decide this?
Spotify makes use of its personal algorithms to research each track in its catalog. For each track, they supply a listing of options related to it. One use of this evaluation is to create playlists and offer you suggestions. The excellent news is that their API gives entry to those analyses via the audio_features endpoint, permitting you to entry all of the options of any track.
For instance, let’s analyze the audio options of the well-known track “Macarena,” which I’m positive everybody is aware of. I received’t cowl each parameter of the observe intimately, however let’s give attention to one side to raised perceive the way it works — the danceability rating of 0.823.
In accordance with Spotify’s documentation, danceability describes how appropriate a observe is for dancing primarily based on a mix of musical parts, together with tempo, rhythm stability, beat power, and total regularity. A rating of 0.0 is the least danceable, and 1.0 is essentially the most danceable. With a rating of 0.823 (or 82.3%), it’s straightforward to say that this observe may be very danceable.
The Three Temporalities
Earlier than going additional, I must introduce an idea with the Spotify API referred to as time_range. This attention-grabbing parameter means that you can retrieve knowledge from completely different time intervals by specifying the time_range:
- short_term: the final 4 weeks of listening exercise
- medium_term: the final 6 months of listening exercise
- long_term: your complete lifetime of your listening exercise
Let’s illustrate this with an instance: if you wish to get your high 10 tracks from the final 4 weeks, you’ll be able to name the corresponding endpoint and cross the time_range as a parameter like this : https://api.spotify.com/v1/me/top/artists?time_range=short_term&limit=10
Calling this provides you with your high 10 artists from the previous month.
With all this info obtainable, my thought was to create an information product that permits customers to grasp what they’re listening to, and to detect variations of their temper by evaluating completely different temporalities. This evaluation can then present how modifications in our lives are mirrored in our music selections.
For instance, I just lately began operating once more, and this variation in my routine has affected my music preferences. I now take heed to music that’s quicker and extra energetic than what I usually listened to up to now. That’s my interpretation, after all, however it’s attention-grabbing to see how a change in my bodily exercise can have an effect on what I take heed to.
This is only one instance, as everybody’s musical journey is exclusive and may be interpreted otherwise primarily based on private experiences and life modifications. By analyzing these patterns, I believe it’s fairly cool to have the ability to make connections between our life-style selections and the music that we prefer to take heed to.
Making Information Perception Accessible
The deeper I acquired into this undertaking, the extra I got here to appreciate that, sure, I may analyze my knowledge and are available to sure conclusions myself, however I wished everybody to do it.
To me, the best technique to share knowledge insights with non-technical folks and make it so very accessible isn’t via a elaborate BI dashboard. My thought was to create one thing universally accessible, which led me to develop a mobile-friendly net software that anybody may use.
To make use of the app, all you want is a Spotify account, join it to BeatBuddy with the press of 1 button, and also you’re finished !
Measuring Musical Feelings
Let’s take a look at one other characteristic of the app: measuring the happiness degree of the music you’re listening to, which may replicate your present temper. The app aggregates knowledge out of your current high tracks, specializing in the ‘valence’ parameter, which represents musical happiness, with 1 being tremendous completely satisfied music. As an example, if the common valence of your present tracks is 0.432, and your all-time common is 0.645, it would recommend a shift in direction of extra melancholic music just lately.
Nevertheless, these analyses needs to be taken with a grain of salt, as these numbers symbolize tendencies quite than absolute truths. Generally, we shouldn’t at all times attempt to discover a motive behind these numbers.
For instance, should you had been monitoring your strolling tempo and found you might have been strolling quicker recently, it doesn’t essentially imply you’re in additional of a rush — it could possibly be as a consequence of varied minor components like modifications in climate, new sneakers, or just a unconscious shift. Generally modifications happen with out specific causes, and whereas it’s potential to measure these variations, they don’t at all times require simple explanations.
That being stated, noticing important modifications in your music listening habits may be attention-grabbing. It might assist you concentrate on how your emotional state or life state of affairs is likely to be affecting your musical preferences. This side of BeatBuddy provides an attention-grabbing perspective, though it’s price noting that these interpretations are just one piece of the advanced puzzle of our feelings and experiences
Let’s be sincere, analyzing your listening habits is one factor, however how do you’re taking motion primarily based on this evaluation? In the long run, making data-driven selections is the final word objective of knowledge evaluation. That is the place suggestions come into play.
Suggestions Primarily based on Your Chosen Temper
An attention-grabbing characteristic of BeatBuddy is its capability to supply music suggestions primarily based on a temper you choose and the music you want.
As an example, you would possibly notice that what you might be listening to has a rating of 75% recognition (which is sort of excessive), and also you wish to discover hidden gems tailor-made to your tastes. You’ll be able to then tweak the “Recognition” slider to, say, 25% to create a recent playlist with a mean rating of 25% recognition.
Behind the scenes, there’s an API name to Spotify’s algorithm to create a suggestion primarily based on the factors you’ve chosen. This name generates a playlist suggestion tailor-made to each your preferences and the temper parameters you’ve set. It makes use of your high 5 current tracks to fine-tune Spotify’s suggestion algorithm in accordance with your selections.
When you’re proud of the playlist, it can save you it on to your Spotify library. Every playlist comes with an outline that particulars the parameters you selected, serving to you bear in mind the temper every playlist is supposed to evoke.
Growing an online software that analyzes Spotify knowledge has been a difficult however rewarding journey. I’ve been pushed out of my consolation zone and gained data in a number of areas, together with net API, cookie administration, net safety, OAuth2, front-end growth, cell optimization, and website positioning. Under is a diagram of the high-level structure of the applying:
My preliminary objective was to begin a modest knowledge undertaking to research my listening habits. Nevertheless, it become a three-month undertaking wealthy in studying and discovery.
All through the method, I spotted how carefully associated knowledge evaluation and net growth are, particularly in terms of delivering an answer that’s not solely practical but additionally user-friendly and simply accessible. In the long run, software program growth is actually about shifting knowledge from one place to a different.
One final observe: I wished to create an software that was clear and offered a seamless person expertise. That’s the reason BeatBuddy is totally ad-free, no knowledge is offered or shared with any third events. I’ve created this with the only goal of giving customers a technique to higher perceive their music selections and uncover new tracks.
You may give the app a strive right here: https://www.beatbuddy.cloud
If in case you have any feedback or strategies, I’m all ears! Your suggestions is absolutely essential.
For these all for a deeper dive, maintain an eye fixed out for my upcoming article.
Cheers!
Lazare