Deep Learning Neural Networks for Music Information Retrieval

Music Information Retrieval is a vast area of study and research that deals with understanding the different features of soundtracks and being able to classify, group and modify them based on it. It has multiple sub fields like Music Classification, Generation, Recommendation, Processing, Recognitio...

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Bibliographic Details
Published in2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) pp. 500 - 503
Main Authors Singh, Manya, Jha, Sanjeev Kumar, Singh, Bhopendra, Rajput, Banafsha
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.03.2021
Subjects
Classification algorithms
Computer architecture
Deep learning
Knowledge engineering
Music information retrieval
Neural networks
Optimization
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Summary:Music Information Retrieval is a vast area of study and research that deals with understanding the different features of soundtracks and being able to classify, group and modify them based on it. It has multiple sub fields like Music Classification, Generation, Recommendation, Processing, Recognition etc. In this project, I focused only on Music Classification. This paper's main objective is to compare two different neural network architectures used in examining the genre of music. It employs the use of different parameters and types of optimization algorithms which helps us analyze which will help achieve higher accuracy.
DOI:10.1109/ICCIKE51210.2021.9410732