A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files
The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metr...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
29.05.2019
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Subjects | |
Online Access | Get full text |
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Summary: | The development of models for learning music similarity and feature
extraction from audio media files is an increasingly important task for the
entertainment industry. This work proposes a novel music classification model
based on metric learning and feature extraction from MP3 audio files. The
metric learning process considers the learning of a set of parameterized
distances employing a structured prediction approach from a set of MP3 audio
files containing several music genres. The main objective of this work is to
make possible learning a personalized metric for each customer. To extract the
acoustic information we use the Mel-Frequency Cepstral Coefficient (MFCC) and
make a dimensionality reduction with the use of Principal Components Analysis.
We attest the model validity performing a set of experiments and comparing the
training and testing results with baseline algorithms, such as K-means and Soft
Margin Linear Support Vector Machine (SVM). Experiments show promising results
and encourage the future development of an online version of the learning
model. |
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DOI: | 10.48550/arxiv.1905.12804 |