Raga is a fundamental melodic concept in Indian Art Music (IAM). It is characterized by complex patterns. All performances and compositions are based on the raga framework. Raga and tonic detection have been a long-standing research problem in the field of Music Information Retrieval. In this paper, we attempt to detect the raga using a novel feature to extract sequential or temporal information from an audio sample. We call these Sequential Pitch Distributions (SPD), which are distributions taken over pitch values between two given pitch values over time. We also achieve state-of-the-art results on both Hindustani and Carnatic music raga data sets with an accuracy of 99% and 88.13%, respectively. SPD gives a great boost in accuracy over a standard pitch distribution. The main goal of this paper, however, is to present an alternative approach to modeling the temporal aspects of the melody and thereby deducing the raga.

翻译：Raga（拉格）是印度艺术音乐（IAM）中的基础旋律概念，其以复杂模式为特征。所有表演和创作均基于Raga框架。Raga与基音检测一直是音乐信息检索领域的长期研究课题。本文尝试通过提取音频样本中的序列或时间信息的新型特征来检测Raga。我们将其命名为序列音高分布（SPD），即随时间在两个给定音高值之间采样的音高分布。我们在印度斯坦和卡纳提克音乐Raga数据集上均取得了当前最优结果，准确率分别达到99%和88.13%。相较于标准音高分布，SPD显著提升了准确率。然而，本文的核心目标是提出一种建模旋律时间维度的替代方法，并由此推断Raga。