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An automatic mixing system for multitrack spatialization for stereo based on unmasking and best panning practices

机译:一种基于混音和最佳平移实践的立体声多轨空间化自动混音系统

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One of the most important tasks in audio production is to place sound sources across the stereo field so as to reduce masking and immerse the listener within the space. This process of panning sources of a multitrack recording to achieve spatialization and masking minimization is a challenging optimization problem, mainly because of the complexity of auditory perception. We propose a novel panning system that makes use of a common framework for spectral decomposition, masking detection, multitrack sub-grouping and frequency-based spreading. It creates a well spatialized mix with increased clarity while complying to the best panning practices. Both real-time and offline optimization-based approaches arc designed and implemented. We investigate the reduction of inter-track auditory masking using the MPEG psychoacoustic model along with various other masking and spatialization metrics, extended for multitrack content. Subjective and objective tests compare the proposed work against mixes by professional sound engineers and existing auto-mix systems.
机译:音频制作中最重要的任务之一是将声源放置在整个立体声场上,以减少掩盖并使听众沉浸在空间中。平移多轨记录的源以实现空间化和蒙版最小化的过程是一个具有挑战性的优化问题,主要是因为听觉感知的复杂性。我们提出了一种新颖的平移系统,该系统使用了用于频谱分解,掩蔽检测,多轨子分组和基于频率的扩展的通用框架。它遵循最佳平移惯例,创建了具有良好清晰度的良好空间混合效果。基于实时和脱机优化的方法均已设计和实现。我们研究了使用MPEG心理声学模型以及各种其他掩蔽和空间化指标(适用于多轨内容)扩展的轨道间听觉掩蔽的减少情况。主观和客观测试将建议的工作与专业声音工程师和现有自动混音系统的混音进行比较。

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