Music genre recognition - Kivy mobile app
Budget: €10 – €60 EUR
I have a mobile application developed in python (using kivy for the front end and SQLite for the database) which currently has the song recognition part implemented. Next, I want the application to fulfill the function of recognizing the musical genres related to the recognized songs. In principle, the application works like Shazam: the user presses a button and plays a song.
If the song is not recognized, a corresponding message appears on the screen, and if it is recognized the user is redirected to a screen that represents a music player that is used to play the song, but also to navigate to a new screen.
There we have 2 tabs already created: one that contains details about the song, one with the related lyrics, and I want the third one to contain a button. After pressing it, a graphic should be displayed on the screen to illustrate in percentage the musical genres identified in the respective song. In principle, I thought of using librosa and tensorflow, but it is not mandatory, it is important to achieve functionality. In addition, I would like to add a functionality for downloading songs that are recognized. We will discuss all the details in private. Thank you!
If the song is not recognized, a corresponding message appears on the screen, and if it is recognized the user is redirected to a screen that represents a music player that is used to play the song, but also to navigate to a new screen.
There we have 2 tabs already created: one that contains details about the song, one with the related lyrics, and I want the third one to contain a button. After pressing it, a graphic should be displayed on the screen to illustrate in percentage the musical genres identified in the respective song. In principle, I thought of using librosa and tensorflow, but it is not mandatory, it is important to achieve functionality. In addition, I would like to add a functionality for downloading songs that are recognized. We will discuss all the details in private. Thank you!