Emmanuel and Yao understood the subject in a different way.
We decided to chose the other subject, since theirs didn't seem quite relevant to our topic.
What is 1Place2Perform ?
1PLACE2PERFORM is an app that links musicians to places such as pubs, bars, restaurants... in which they can perform. In such places, musicians perform on stage in front of an audience that has the same musical taste as them.
In their professional context, musicians do not really benefit from a prodund career guidance. They struggle to find places in which they can perform in front of an audience adapted to their style.
There is a real need. We want to create an app dedicated to musicians.
The generation of the idea
Concerning the definitions linked to the project, after our discussion with Théo, we understood that our project wasn't fully based on machine learning.
Indeed Apolline, Quentin and Agathe had, from their work, the idea of creating a platform where musician can fully live from their passion.
But it wasn't really linked to machine learning
So, we all investigated about machine learning, to understand the possibilities and opportunities offered by this new technology.
This video was very helpful. Indeed, they use the example of machine learning to find music. So we knew that there were a real chance for us to create a great app.
Afterall, we choose to focus on the project 1PLACE2PERFORM because this one was much more complete for musicians. Furthermore, the user test conducted on HackMD was also truly interesting for us. The app seemed to be very useful for users.
Here are the example of the questionary we did :
Then we searched on the internet whether we can find some concurrence and try to differentiate our app from them.
To differentiate ourselves, we have to deeply use machine learning for the musicians to be fully satisfied.
In this case, we haven't found any application that provide a qualitative service as we do.
This is what we have done. Indeed, we have redesigned the app on Figma to respect the recommandations of our interviewers and also to differentiate ourselves from the competitiors.
Thanks to our wireframe, we understood that we had a lot of thing to deal with before lauching the app. For example, testing.
In fact, as we said before, machine learning can detect music and thousands of millions of different styles (like Shazam does for music).
But it has to be related to musicians playing and not to a song
So why did we do?
Why don't we add a microphone in order to allow musicians to record themselves, thanks to machine learning ?
Then, they have to search and find the right bar, taking into account their location. This wasn't that difficult because we copied the idea of uber to book a cab.
Finally, creating relationships and contacts between bars and musicians is not that hard, because they both find an interest on this kind of partnership.
We used the idea of uber to put in relation bars and musicians. However we need the expertise of developpers to help us creating the map, in which musicians can be geolocated and also the bars around them.
Moreover we need developpers to ensure the safety of the app. In fact on the app you can find plenty of personnal informations like phone number...
