Large scale machine learning-based Raga identification continues to be a nontrivial issue in the computational aspects behind Carnatic music. Each raga consists of many unique and intrinsic melodic patterns that can be used to easily identify them from others. These ragas can also then be used to cluster songs within the same raga, as well as identify songs in other closely related ragas. In this case, the input sound is analyzed using a combination of steps including using a Discrete Fourier transformation and using Triangular Filtering to create custom bins of possible notes, extracting features from the presence of particular notes or lack thereof. Using a combination of Neural Networks including 1D Convolutional Neural Networks conventionally known as Time-Delay Neural Networks) and Long Short-Term Memory (LSTM), which are a form of Recurrent Neural Networks, the backbone of the classification strategy to build the model can be created. In addition, to help with variations in shruti, a long-time attention-based mechanism will be implemented to determine the relative changes in frequency rather than the absolute differences. This will provide a much more meaningful data point when training audio clips in different shrutis. To evaluate the accuracy of the classifier, a dataset of 676 recordings is used. The songs are distributed across the list of ragas. The goal of this program is to be able to effectively and efficiently label a much wider range of audio clips in more shrutis, ragas, and with more background noise.

翻译：基于机器学习的大规模拉格识别在卡纳提克音乐的计算分析中仍然是一个重要挑战。每个拉格包含众多独特的内在旋律模式，这些模式可用于将其与其他拉格区分开来。这些拉格还可用于聚类同一拉格内的歌曲，并识别其他密切关联拉格中的歌曲。本系统通过多步骤分析输入音频：首先采用离散傅里叶变换与三角滤波相结合的方法，构建可能音符的自定义频段；随后根据特定音符的存在与否进行特征提取。分类策略的核心模型采用神经网络组合架构，包括传统称为时延神经网络的一维卷积神经网络（1D CNN）以及作为循环神经网络变体的长短期记忆网络（LSTM）。此外，为应对什鲁蒂音高的变化，系统引入了长时注意力机制以捕捉频率的相对变化而非绝对差异，这将为在不同什鲁蒂音高下训练音频片段提供更具意义的数据特征。为评估分类器精度，研究采用包含676条录音的数据集，歌曲均匀分布于各拉格类别。本项目的目标在于实现对更广泛什鲁蒂音高、更多拉格类型及更高背景噪声环境下的音频片段进行高效准确的标注。