On the Importance of Temporally Precise Onset Annotations for Real-Time Music Information Retrieval: Findings from the AG-PT-set Dataset

In real-time Music Information Retrieval (MIR), small analysis windows are essential for achieving low retrieval latency. In turn, event-based real-time MIR methods require precise onset detectors to correctly align with the beginning of events such as musical notes. Detectors are typically trained using ground-truth annotations from datasets of interest. Yet, most MIR datasets do not prioritize the accurate timing of onset labels, and the evaluation of detectors often relies on generous tolerance windows (even ±50ms). In this paper we present AG-PT-set, a new dataset of acoustic guitar techniques with precise onset annotations. The dataset features 32,592 individual notes and over 10 hours of audio, covering eight techniques. Moreover, we assess the importance of exact onset labels across multiple real-time MIR tasks. Our results show how accurate timing of onset labels and precise detectors are crucial for real-time MIR tasks, as the performance of most algorithms degrades with imprecise onsets. In few occasions, imprecise onset timing slightly improved results, hinting at a possible similarity to data augmentation methods. Taken together, our findings indicate that temporally precise labels and detectors are always preferable, as robustness can always be obtained via artificial augmentation, while precision cannot be obtained as easily