第十届电声技能国际钻研会（ISEAT）将在11月8-9日于深圳举办，请列位佳宾、参会人士注意如下要害信息：

11月8-9日两天，年夜会于一至四号陈诉厅、沉浸声体验空间同时举行1000+分钟的演讲陈诉，举办两个专题论坛：汽车声学立异技能论坛（3小时）、好声音论坛（2小时），并特设两个重磅陈诉，来自Journal of AES主编Prof. Vesa Välimäki、南京年夜学沈勇传授 ，与年夜会主题辞“沉浸于好声音中”高度契合。为此发布《集会议程》2.0版。

This keynote talk provides an overview of graphic equalizers (EQ). To be effective, any intelligent graphic EQ method must rely on automatic design that closely approximates the target magnitude response. Today, we can design highly accurate cascade and parallel graphic EQ filters. Historically, however, graphic EQ designs have been surprisingly inaccurate. A cascade graphic EQ consists of a chain of parametric EQ filters, which may be based on various alternative coefficient formulas. This presentation demonstrates that the choice of parametric EQ design significantly impacts the accuracy of the graphic EQ, as it influences the interaction between filter bands. The filter gains used for filtering must differ from the target gains used for design and can be optimized using the least squares (LS) method, provided that the band filters maintain sufficient self-similarity with different gain values. Designing a parallel graphic EQ system accurately is more challenging than designing a cascade one, because it requires consideration of the phase response of each band filter. Fortunately, a series-to-parallel conversion technique offers a straightforward solution: converting a cascade EQ design into a parallel one. The use of neural networks to control a graphic EQ is also discussed. Additionally, recent approaches that use shelving filters to design graphic EQs or a single first-order shelving filter to enhance their accuracy range are su妹妹arized. Finally, an application example of perceptual headphone equalization for listening in environments with heavy background noise is presented.

于剧院（年夜空间）、家居客堂（中空间）、车舱（小空间）与可穿着装备（微空间）这四年夜典型场景中，实现真正意义上的“沉浸式好声音”，是一项布满挑战的体系工程。

南京年夜学电声与建声课题组，深切剖析这四类场景的焦点特性，总结出一套体系性的M-P-C-R要领。该方案可以或许有用支撑繁杂沉浸声体系的研发，破解多参数耦合带来的工程难题，实现可范围化、工程化的沉浸声效构建，领悟了从基础研究到集成运用验证的全流程。

M-P-C-R要领，素质上是一套笼罩扩声或者重放全链路的完备解决方案，提供从丈量、猜测、节制到感知优化的“技能全家桶”，鞭策声学设计从依靠经验，慢慢转向以数据与感知量化为基础的全新范式。这一转型的要害，于在将主不雅听觉感触感染转化为可量化、可调控的参数系统，为实现更可控、更可猜测的沉浸式声音体验提供坚实支撑。

想深切相识这一“技能全家桶”？11月9日上午到二号陈诉厅凝听陈诉：怎样得到并量化“沉浸式好声音”？

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