论文标题
音频预处理技术和RAGA识别深度学习算法的比较
A Comparison of Audio Preprocessing Techniques and Deep Learning Algorithms for Raga Recognition
论文作者
论文摘要
拉加斯构成了印度古典音乐的基础。最近，最近在音乐信息检索社区中，Raga识别的任务在最近的音乐检索社区中获得了吸引力，这可以归因于印度古典音乐的细微差别，这导致了很多计算机研究问题。在这项工作中，我们使用了两种不同的数字音频信号处理技术来预处理carnatic古典拉加斯的音频样本，然后通过各种深度学习模型对其进行处理。比较了他们的结果，以推断哪种DASP技术更适合Raga识别任务。我们获得了最新的结果，我们的最佳模型达到了98.1％的测试精度。我们还比较了每个模型能够区分相似的拉加斯的能力。
Ragas form the foundation for Indian Classical Music. The task of Raga Recognition has gained traction in the Music Information Retrieval community in the recent past, which can be attributed to the nuances of Indian Classical Music that have resulted in a plethora of research problems in Computing. In this work, we used two different digital audio signal processing techniques to preprocess audio samples of Carnatic classical ragas that were then processed by various Deep Learning models. Their results were compared in order to infer which DASP technique is better suited to the task of raga recognition. We obtained state of the art results, with our best model reaching a testing accuracy of 98.1%. We also compared each model ability to distinguish between similar ragas.
