Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness

TL;DR

This paper enhances real-time opera tracking by integrating audio event detectors with Dynamic Time-Warping alignment, significantly improving robustness in live full opera performances.

Contribution

It introduces a novel combination of DTW-based music tracking with specialized audio event detectors to address opera-specific challenges in live score following.

Findings

Improved robustness in opera tracking through combined methods

Identification of key error sources in live opera tracking

Demonstration of step-by-step integration of detectors with DTW

Abstract

Recent advances in real-time music score following have made it possible for machines to automatically track highly complex polyphonic music, including full orchestra performances. In this paper, we attempt to take this to an even higher level, namely, live tracking of full operas. We first apply a state-of-the-art audio alignment method based on online Dynamic Time-Warping (OLTW) to full-length recordings of a Mozart opera and, analyzing the tracker's most severe errors, identify three common sources of problems specific to the opera scenario. To address these, we propose a combination of a DTW-based music tracker with specialized audio event detectors (for applause, silence/noise, and speech) that condition the DTW algorithm in a top-down fashion, and show, step by step, how these detectors add robustness to the score follower. However, there remain a number of open problems which we identify as targets for ongoing and future research.