Improving Real-time Score Following in Opera by Combining Music with Lyrics Tracking

Fully automatic opera tracking is challenging because of the acoustic complexity of the genre, combining musical and linguistic information (singing, speech) in complex ways. In this paper, we propose a new pipeline for complete opera tracking. The pipeline is based on two trackers. A music tracker that has proven to be effective at tracking orchestral parts, will lead the tracking process. In addition, a lyrics tracker, that has recently been shown to reliably track the lyrics of opera songs, will correct the music tracker when tracking parts that have a text dominance over the music. We will demonstrate the efficiency of this method on the opera Don Giovanni, showing that this technique helps improving accuracy and robustness of a complete opera tracker.

On-Line Audio-to-Lyrics Alignment Based on a Reference Performance

Audio-to-lyrics alignment has become an increasingly active research task in MIR, supported by the emergence of several open-source datasets of audio recordings with word-level lyrics annotations. However, there are still a number of open problems, such as a lack of robustness in the face of severe duration mismatches between audio and lyrics representation; a certain degree of language-specificity caused by acoustic differences across languages; and the fact that most successful methods in the field are not suited to work in real-time. Real-time lyrics alignment (tracking) would have many useful applications, such as fully automated subtitle display in live concerts and opera. In this work, we describe the first real-time-capable audio-to-lyrics alignment pipeline that is able to robustly track the lyrics of different languages, without additional language information. The proposed model predicts, for each audio frame, a probability vector over (European) phoneme classes, using a very small temporal context, and aligns this vector with a phoneme posteriogram matrix computed beforehand from another recording of the same work, which serves as a reference and a proxy to the written-out lyrics. We evaluate our system's tracking accuracy on the challenging genre of classical opera. Finally, robustness to out-of-training languages is demonstrated in an experiment on Jingju (Beijing opera).

Handling Structural Mismatches in Real-time Opera Tracking

Algorithms for reliable real-time score following in live opera promise a lot of useful applications such as automatic subtitles display, or real-time video cutting in live streaming. Until now, such systems were based on the strong assumption that an opera performance follows the structure of the score linearly. However, this is rarely the case in practice, because of different opera versions and directors' cutting choices. In this paper, we propose a two-level solution to this problem. We introduce a real-time-capable, high-resolution (HR) tracker that can handle jumps or repetitions at specific locations provided to it. We then combine this with an additional low-resolution (LR) tracker that can handle all sorts of mismatches that can occur at any time, with some imprecision, and can re-direct the HR tracker if the latter is `lost' in the score. We show that the combination of the two improves tracking robustness in the presence of strong structural mismatches.

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

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.