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Audio Instruments Identification Using CNN and XGBoost

Dahiya, Akshay (2019) Audio Instruments Identification Using CNN and XGBoost. Masters thesis, Dublin, National College of Ireland.

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This paper is brief research on how to identify the audio instruments using machine learning. Algorithms of machine learning and artificial intelligence systems are sky boosting the music industries. With the help of machine learning, problems can be resolved with a much better solution. Creating music artificially using state of art software’s likes FL Studio, tractor, Soundation and many others software for producing the digital and repeated pattern instrument sounds are being used in creation of music. Hence, music produced digitally, at different Beats Per Minute (BPM) therefore, it is now easy to recognize what kind of music is playing by using certain applications like Shazam, Spotify, Sony Track ID for identification of the music artist and the genre of the music. But finding instruments that were used to create the music is quite difficult. Sometimes even the applications which is used for identification of music , playing outside on speaker shows wrong results because of the working algorithm behind the applications do not understand the parallel pattern of the audio clips. As many instruments are used to produce the proper audio file so it is difficult to read the pattern of the instrument for the machine. That’s the real game in this research. After training the data, which are audio clips of many instruments, this model can recognize the audio instruments in the audio clips of the music. This will help to boost in applications development like Shazam, Spotify, Sony Track ID and to music developers, producers to improve their skills.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Music Industry
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 14 Oct 2019 08:41
Last Modified: 14 Oct 2019 08:41

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