Abstract
Beat and downbeat tracking, jointly referred to as Meter Tracking, is a fundamental task in Music Information Retrieval (MIR). Deep learning models have far surpassed traditional signal processing and classical machine learning approaches in this domain, particularly for Western (Eurogenetic) genres, where large annotated datasets are widely available. These systems, however, perform less reliably on underrepresented musical traditions. Carnatic music, a rich tradition from the Indian subcontinent, is renowned for its rhythmic intricacy and unique metrical structures (tÄlas). The most notable prior work on meter tracking in this context employed probabilistic Dynamic Bayesian Networks (DBNs). The performance of state-of-the-art (SOTA) deep learning models on Carnatic music, however, remains largely unexplored. In this study, we evaluate two models for meter tracking in Carnatic music: the Temporal Convolutional Network (TCN), a lightweight architecture that has been successfully adapted for Latin rhythms, and Beat This!, a transformer-based model designed for broad stylistic coverage without the need for post-processing. Replicating the experimental setup of the DBN baseline on the Carnatic Music Rhythm (CMR$_f$) dataset, we systematically assess the performance of these models in a directly comparable setting. We further investigate adaptation strategies, including fine-tuning the models on Carnatic data and the use of musically informed parameters. Results show that while off-the-shelf models do not always outperform the DBN, their performance improves substantially with transfer learning, matching or surpassing the baseline. These findings indicate that SOTA deep learning models can be effectively adapted to underrepresented traditions, paving the way for more inclusive and broadly applicable meter tracking systems.
Abstract (translated)
节拍和弱拍跟踪，统称为节奏跟踪（Meter Tracking），是音乐信息检索（MIR）中的基本任务之一。在西方（欧洲传统）音乐流派中，深度学习模型已经远远超越了传统的信号处理和经典机器学习方法，尤其是在有大量标注数据集可用的情况下。然而，这些系统在代表性不足的音乐传统上表现得不那么可靠。卡纳提克音乐是一种来自印度次大陆的丰富传统，以其复杂的节奏结构（tÄlas）而闻名。在此领域的先前研究中，最著名的工作之一是使用概率动态贝叶斯网络（DBNs）。然而，最先进的深度学习模型在卡纳提克音乐上的表现仍然未被充分探索。 在这项研究中，我们评估了两种用于卡纳提克音乐节奏跟踪的模型：时序卷积网络（TCN），这是一种轻量级架构，在拉丁节奏上已经成功地进行了适应；以及 Beat This!，这是一个基于变压器的模型，旨在覆盖广泛的风格，并且无需后期处理。我们通过在卡纳提克音乐节奏（CMR$_f$）数据集上复制DBN基线实验设置，系统评估了这些模型在一个可以直接比较的环境中表现。 此外，我们还探讨了适应策略，包括在卡纳提克数据上微调模型以及使用受音乐启发的信息参数。结果表明，虽然现成的模型并不总能超越DBN性能，但通过迁移学习其性能显著提升，达到了与基线相匹配或超过基线的水平。这些发现表明，最先进的深度学习模型可以有效地适应代表性不足的传统，为更包容和广泛适用的节奏跟踪系统铺平道路。
URL
https://arxiv.org/abs/2509.11241
