基于拉格的印度古典音乐深度学习分类

Anupam Singha, N. R. Rajalakshmi, J. Arun Pandian, Swaminathan Saravanan
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引用次数: 1

摘要

古典音乐是印度文化谱系中不可或缺的一部分。拉格是所有印度古典音乐的基础,包括印度斯坦音乐和卡纳蒂克音乐。“拉格”一词通常用于指印度古典音乐中的旋律结构。传统的ragas分类方法既耗时又低效。在这项工作中,提出了一种卷积神经网络用于拉格分类。所提出的卷积神经网络在70类拉格上进行了训练。所提出的卷积神经网络获取音频音符的频谱图,并识别音符的拉格。所提出的卷积神经网络模型在拉格分类上的准确率达到97.9%。使用精度、召回率、F1分数和AUC-ROC等标准性能指标,将所提出的卷积神经网络的性能与传统分类技术进行比较。对比结果表明,所提出的卷积神经网络的分类性能优于当前最先进的机器学习技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Classification of Indian Classical Music Based on Raga
Classical music is an integral part of Indian culture’s lineage. The raga serves as the foundation for all Indian classical music, including both Hindustani and Carnatic music. The term “raga” is typically used to refer to the melodic structure in Indian classical music. Traditional approaches to the classification of ragas are time consuming and inefficient. In this work, a convolutional neural network was proposed for raga classification. The proposed convolutional neural network was trained on 70 classes of ragas. The proposed convolutional neural network takes the spectrograms of the audio note and identifies the raga of the note. The proposed convolutional neural network model achieved a precision of 97.9% on raga classification. The performance of the proposed convolutional neural network was compared with the traditional classification techniques using standard performance metrics such as precision, recall, F1 score, and AUC-ROC. The comparison results show that the classification performance of the proposed convolutional neural network was superior to the state-of-the-art machine learning techniques.
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