SynthSOD:为管弦乐队音乐源分离开发异构数据集

Jaime Garcia-Martinez, David Diaz-Guerra, Archontis Politis, Tuomas Virtanen, Julio J. Carabias-Orti, Pedro Vera-Candeas
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引用次数: 0

摘要

近年来,音乐音源分离技术取得了长足进步,特别是从混合音轨中分离人声、鼓声和低音元素方面。然而,从管弦乐队录音中提取类似音源的挑战尚未得到广泛探索,这主要是由于缺乏全面、干净(即无噪声)的多轨数据集。在本文中,我们介绍了一种名为 SynthSOD 的新型多轨数据集,该数据集采用一系列模拟技术来创建一个真实的(即使用高质量音色字体)、有音乐动机的异质训练集,其中包括不同的动态、自然的节奏变化、风格和条件。此外,我们还演示了在我们的合成数据集上训练的广泛使用的基线音乐分离模型在著名的 EnsembleSet 上的应用,并评估了其在合成和真实世界条件下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe much to the creation and use of large-scale, multitrack datasets dedicated to these specific components. However, the challenge of extracting similarly sounding sources from orchestra recordings has not been extensively explored, largely due to a scarcity of comprehensive and clean (i.e bleed-free) multitrack datasets. In this paper, we introduce a novel multitrack dataset called SynthSOD, developed using a set of simulation techniques to create a realistic (i.e. using high-quality soundfonts), musically motivated, and heterogeneous training set comprising different dynamics, natural tempo changes, styles, and conditions. Moreover, we demonstrate the application of a widely used baseline music separation model trained on our synthesized dataset w.r.t to the well-known EnsembleSet, and evaluate its performance under both synthetic and real-world conditions.
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