Music Genre Classification Using Data Filtering Algorithm: An Artificial Intelligence Approach

Anirudh Ghildiyal, Sachin Sharma
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引用次数: 4

Abstract

The rise of music industry across the globe can be seen with the new type of genre being created, and more artist and musicians joining this profession. A lot of music is created and launched every day. A major task for various music streaming platform is to classify these songs based on the genres and recommend music to the users. To overcome this many artificial intelligence algorithms are developed. One of the major problems in designing machine learning models is inadequate data for training. Certain dataset contains lot of redundant features that could cause the models to overfit. This problem could be resolved by data filtering. This paper has developed the multiple Artificial Intelligence (AI) models and applied data filtering method on the GTZAN dataset for music genre classification. A comparative analysis is done and discussed in this paper.
基于数据过滤算法的音乐类型分类:一种人工智能方法
音乐产业在全球范围内的崛起,可以看到新的流派被创造出来,越来越多的艺术家和音乐家加入这个行业。每天都有很多音乐被创作和发布。各种音乐流媒体平台的一个主要任务是根据音乐类型对这些歌曲进行分类并向用户推荐音乐。为了克服这个问题,许多人工智能算法被开发出来。设计机器学习模型的主要问题之一是用于训练的数据不足。某些数据集包含大量冗余特征,可能导致模型过拟合。这个问题可以通过数据过滤来解决。本文开发了多个人工智能模型,并在GTZAN数据集上应用数据过滤方法进行音乐类型分类。本文对此进行了比较分析和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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