论文标题
迈向可靠的实时歌剧跟踪：将一致性与音频事件探测器相结合以提高鲁棒性
Towards Reliable Real-time Opera Tracking: Combining Alignment with Audio Event Detectors to Increase Robustness
论文作者
论文摘要
实时音乐得分的最新进展使机器可以自动跟踪高度复杂的复杂音乐，包括完整的乐团表演。在本文中，我们试图将其提高到更高的水平，即对完整歌剧的实时跟踪。我们首先将基于在线动态时间巡游（OLTW）的最先进的音频对准方法应用于莫扎特歌剧的全长记录，并分析跟踪器最严重的错误，确定了特定于歌剧情景的三个常见问题来源。为了解决这些问题，我们提出了将基于DTW的音乐跟踪器与专业音频事件探测器（为了鼓掌，沉默/噪音和语音）的组合，该探测器以自上而下的方式调节DTW算法，并逐步展示这些探测器如何为得分追随者增加健壮性。但是，我们仍然存在许多开放问题，我们将其确定为正在进行的和未来研究的目标。
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.
