results: 论文提供了声音生成的多种示例，包括单变量数据的多种表示方式、多变量数据的声音映射和全息声音示例，以及未来功能的概述。Abstract
We introduce STRAUSS (Sonification Tools and Resources for Analysis Using Sound Synthesis) a modular, self-contained and flexible Python sonification package, operating in a free and open source (FOSS) capacity. STRAUSS is intended to be a flexible tool suitable for both scientific data exploration and analysis as well as for producing sonifications that are suitable for public outreach and artistic contexts. We explain the motivations behind STRAUSS, and how these lead to our design choices. We also describe the basic code structure and concepts. We then present output sonification examples, specifically: (1) multiple representations of univariate data (i.e., single data series) for data exploration; (2) how multi-variate data can be mapped onto sound to help interpret how those data variables are related and; (3) a full spatial audio example for immersive Virtual Reality. We summarise, alluding to some of the future functionality as STRAUSS development accelerates.
摘要
我们介绍STRAUSS（对数数据使用 зву频读取的工具和资源），这是一个模块化、自 conten和可变的 Python 声音化套件，在自由开源（FOSS）的容器下运行。STRAUSS 是一个灵活的工具，适用于科学数据探索和分析以及生产适合公共传达和艺术 contexts 的声音化。我们解释了 STRAUSS 的动机和设计选择。我们也描述了代码结构和概念。然后，我们提供了声音化示例，包括：（1）多个表示方法 для univariate 数据（即单一数据系列） для 数据探索;（2）如何将多个变数数据映射到声音中以帮助解释这些数据变数之间的关系;（3）一个完整的 spatial audio 示例，用于具有传真的虚拟现实。我们结束，提到一些未来功能的开发，当 STRAUSS 的开发加速时。
iMagLS: Interaural Level Difference with Magnitude Least-Squares Loss for Optimized First-Order Head-Related Transfer Function
results: 结果表明，ILD 错误可以减少至一定程度，而 HRTF 大小错误保持与MagLS相同水平。这些结果可能对首觉 Ambisonics 的总空间质量产生积极影响，而其他播放方法也可能从 consid Considering 这种修改后的损失函数。Abstract
Binaural reproduction for headphone-based listening is an active research area due to its widespread use in evolving technologies such as augmented and virtual reality (AR and VR). On the one hand, these applications demand high quality spatial audio perception to preserve the sense of immersion. On the other hand, recording devices may only have a few microphones, leading to low-order representations such as first-order Ambisonics (FOA). However, first-order Ambisonics leads to limited externalization and spatial resolution. In this paper, a novel head-related transfer function (HRTF) preprocessing optimization loss is proposed, and is minimized using nonlinear programming. The new method, denoted iMagLS, involves the introduction of an interaural level difference (ILD) error term to the now widely used MagLS optimization loss for the lateral plane angles. Results indicate that the ILD error could be substantially reduced, while the HRTF magnitude error remains similar to that obtained with MagLS. These results could prove beneficial to the overall spatial quality of first-order Ambisonics, while other reproduction methods could also benefit from considering this modified loss.
摘要
《首先采用半耳呈现技术的头戴式播放是当前研究领域的活跃之处，这主要是因为这种技术在虚拟和增强现实（AR和VR）等技术中的广泛使用。一方面，这些应用需要高质量的空间声学感知，以保持听众感到 immerse。但是，记录设备通常只有几个麦克风，这导致FOA（首览声学）的低阶记录。然而，FOA会导致外部化和空间分辨率的局限。在这篇论文中，一种新的头部相关传送函数（HRTF）预处理优化损失函数被提出，该函数添加了Interaural水平差（ILD）错误项，以便与现在广泛使用的MagLS优化损失函数相比。结果表明，ILD错误可以减少到一定程度，而HRTF的大小错误则保持与MagLS相同。这些结果可能有利于首览声学的总空间质量，而其他播放方法也可以从考虑这种修改后得到改善。》Note: Please note that the translation is in Simplified Chinese, which is the standard form of Chinese used in mainland China and Singapore. If you need Traditional Chinese, please let me know.