Weakly Supervised Tabla Stroke Transcription via TI-SDRM: A Rhythm-Aware Lattice Rescoring Framework
Abstract
Tabla Stroke Transcription (TST) is central to the analysis of rhythmic structure in Hindustani classical music, yet remains challenging due to complex rhythmic organization and the scarcity of strongly annotated data. Existing approaches largely rely on fully supervised learning with onset-level annotations, which are costly and impractical at scale. This work addresses TST in a weakly supervised setting, using only symbolic stroke sequences without temporal alignment. We propose a framework that combines a CTC-based acoustic model with sequence-level rhythmic rescoring. The acoustic model produces a decoding lattice, which is refined using a \textbf{$T\bar{a}la$}-Independent Static--Dynamic Rhythmic Model (TI-SDRM) that integrates long-term rhythmic structure with short-term adaptive dynamics through an adaptive interpolation mechanism. We curate a new real-world tabla solo dataset and a complementary synthetic dataset, establishing the first benchmark for weakly supervised TST in Hindustani classical music. Experiments demonstrate consistent and substantial reductions in stroke error rate over acoustic-only decoding, confirming the importance of explicit rhythmic structure for accurate transcription.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a pioneering approach to weakly supervised tabla stroke transcription, establishing a new benchmark and demonstrating the critical role of rhythmic structure in improving transcription accuracy. The innovative combination of acoustic modeling and rhythmic rescoring offers valuable insights into the complexities of music transcription, particularly for non-Western musical traditions.
Methodology
The proposed methodology introduces a novel framework for weakly supervised Tabla Stroke Transcription (TST) that effectively combines a Connectionist Temporal Classification (CTC)-based acoustic model with a rhythm-aware lattice rescoring mechanism. The introduction of the $Tala$-Independent Static-Dynamic Rhythmic Model (TI-SDRM) is a significant advancement, as it integrates long-term rhythmic structure with short-term adaptive dynamics. The adaptive interpolation mechanism used to balance the contributions of static and dynamic models based on acoustic confidence is particularly innovative. However, the methodology could benefit from further clarification on the implementation details of the adaptive interpolation process and how it is tuned in practice.
Results & evaluation
The experimental setup is robust, utilizing both a newly curated real-world tabla dataset and a synthetic dataset to establish a comprehensive benchmark for weakly supervised TST. The results demonstrate substantial improvements in stroke error rates when incorporating the TI-SDRM framework compared to acoustic-only decoding. The ablation studies effectively highlight the contributions of various components within the framework, reinforcing the importance of both long-term rhythmic structure and local adaptation. However, the paper could enhance its experimental rigor by providing more detailed statistical analysis of the results and discussing the significance of the improvements observed.
Reproducibility
The paper provides a thorough description of the methodology, including the acoustic model architecture and training configurations. However, the lack of publicly available code or datasets limits reproducibility. Providing access to the datasets and code would significantly enhance the ability of other researchers to replicate the results and build upon this work.
Limitations
One limitation of the study is the reliance on weakly supervised learning, which, while innovative, may still lead to inaccuracies in transcription due to the absence of precise onset-level annotations. Additionally, the performance of the proposed method may vary across different tabla styles or individual performers, which is not fully explored in the current work. The paper also does not address how the model might generalize to other percussion instruments or musical traditions, which could limit its broader applicability.
Broader impact
The research has the potential to significantly impact the field of music information retrieval, particularly in the context of Indian classical music. By enabling more accessible transcription of tabla performances, it could facilitate music education, archiving, and the preservation of cultural heritage. Furthermore, the methodologies developed could be adapted for use in other musical contexts and instruments, broadening the scope of automatic transcription systems. This paper presents a pioneering approach to weakly supervised tabla stroke transcription, establishing a new benchmark and demonstrating the critical role of rhythmic structure in improving transcription accuracy. The innovative combination of acoustic modeling and rhythmic rescoring offers valuable insights into the complexities of music transcription, particularly for non-Western musical traditions.
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