边音融资 2M 帮助物理AI实现音频感知
Edge Sound Research Inc. 今天的宣布公司有200万美元的融资新资金来建造音频基础设施，将有用的声音隔离开来，并将其与其他传感器数据结合起来，以帮助物理AI系统实时了解所发生的情况。
"暴风雪风险者"领跑这一轮，使公司资金总额达到900万美元。
当公司首次触及这个问题时，它关注体育。 球迷们通过竞技场的扬声器踩在看台上，对于一个试图隔开滴滴的人工智能系统来说，这些重叠的声音可能使这个困难。
这个挑战坐落在Edge Sound Research对机器感知的方法的中心：将音频与跟踪和视觉数据连接起来，以确定哪个声音属于哪个对象。
" 音频中的问题不是以对象为导向，而是以频道为导向，" 联合创始人兼行政长官瓦特里·萨洛马基在一次采访中告诉了SiliconANGLE。
对广播公司来说，建立这种联系意味着在拥挤的竞技场上跟踪玩家的声音。 Edge正在打赌同样的方法可以帮助机器人和其他物理AI系统解释他们周围的世界——包括他们的相机尚无法看到的活动。
人类自动这样做，因为我们存在于一个多模式世界：视觉、声音和身体感觉都会立刻进来在物理AI世界中，声音一直被当作二等公民来对待，因为相机已经无处不在，在日常生活中，用于一切。 对于人类来说，声音是各地航行的根本。
声音通过频道传来，萨洛马基解释。 它与麦克风相连，这些信号可以识别声音 — — 它们不会直接与任何事物相关联，直到其他东西将它们转化为易于识别的信息。
萨洛马基称。 “一个是，你如何创建所有传感器的数字双倍。 另一个是怎样创建AI机器学习模型来源单独的音效元素。”
在运动场演示中，系统使用麦克风位置，皮卡模式和玩家跟踪来选择有用的音频。 然后联系位置；视觉和音频进行配对，以识别物体——如球员，球，甚至教练或带有口哨的裁判。
称公司用户可以导入带有坐标的3D空间或2D布局，并将传感器置于该代表中。 这样可以绘制环境及其传感器的空间图；这基本上是萨洛马基所指的“数字双胞胎”。
其他用途案件可能涉及自动车辆，在警报器接近时，可给予数字耳听器。 人类识别声音和方向，因为我们的耳朵在我们的头上。 如果声音从左边传来，那一边会更响亮
任何听到警笛声接近或退场的人，也都经历过多普勒效应，投球的增减取决于它来得或去得有多快。 即使镜头上看不到灯光。
建立音频感知模型
Edge有它开发的通用模型和微调满足客户需要。 这些模型面临实时活动的不同延迟目标要求。 萨洛马基引述了一种对体育广播的33毫秒的亚容忍，称其他环境可能容忍100或200毫秒。
有用的感知需要及时到达大多数应用程序，因为听到发生的事情需要与看到配合。
该公司正在用其数字双子软件和AI模型建立硬件独立性，使Edge能够围绕核心技术组装新的应用程序。 这样，它将成为音频基础设施，与现有的麦克风和摄像头客户已经为其现有的物理AI机器人，媒体和其他系统一起工作。
有了资金，Salomaki 称，Edge将完成与现有企业客户的一体化，并使平台的部件在A系列之前可以跨行业转让。
现场体育在开发并验证其感知技术时，已经为Edge提供了一个商业市场，利用这种技术帮助培训和建立其模式。 它的Virtual Sound Engine自动捕捉声音，并将声音与复杂环境隔开，已经在整个行业进行了商业部署。 客户和合作伙伴包括美国音频设备制造商Shure Inc.，国家篮球协会，福克斯体育传媒集团和美国网球协会股份有限公司。
“为了实现每个人在物理AI中想要的结果，计算机需要变得更为人性一点，”Salomaki 称。
图片：边缘声音研究
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Edge Sound raises $2M to help physical AI make sense of audio
Edge Sound Research Inc. today announced it has raised $2 million in new funding to build audio infrastructure that isolates useful sounds and combines them with other sensor data to help physical AI systems understand what’s happening in real time.
Stormbreaker Ventures led the round, bringing the company’s total funding to $9 million.
When the company first approached this problem, it looked at sports. When a basketball hits the court, fans are stomping in the stands and bass pounds through the arena’s speakers – all of this comes through microphones at the same time. For an artificial intelligence system trying to isolate the dribble, those overlapping sounds can make this difficult.
This challenge sits at the center of Edge Sound Research’s approach to machine perception: connecting audio with tracking and visual data to determine which sound belongs to which object.
“The issue in audio is it’s not object oriented; it’s channel oriented,” co-founder and Chief Executive Valtteri Salomaki told SiliconANGLE in an interview.
For broadcasters, making that connection means following a player’s sounds through a crowded arena. Edge is betting the same approach could help robots and other physical AI systems interpret the world around them — including activity their cameras cannot yet see.
Humans do this automatically because we exist in a multimodal world: Sight, sound and physical sensation all come in at once. In the physical AI world, sound has always been treated as a second-class citizen to vision because cameras are already everywhere, in everyday life and used for everything. For humans, sound is fundamental to navigating everywhere.
Audio comes in channels, Salomaki explained. It’s connected to microphones, which are positioned around a space or on objects and they listen in locations. These produce signals that identify sounds – they don’t directly associate with anything until something else turns them into readily identifiable information.
“It’s split into two layers,” Salomaki said. “One is how do you create a digital twin of all sensors. The other one is how do you create AI machine learning models to source separate sound elements.”
In the sports arena demonstration, the system uses microphone locations, pickup patterns and player tracking to select useful audio. It then associates locations; visual and audio are paired to identify objects — such as players, the ball, even the coach or a referee with the whistle.
The company said users can import 3D spaces or 2D layouts with coordinates and place sensors within that representation. This allows a spatial map of the environment and its sensors; this is essentially the “digital twin” that Salomaki is referring to.
Other use cases could involve autonomous vehicles, which could be given digital ears to hear if a siren is approaching. Humans identify sounds and direction because our ears are on the sides of our heads. If a sound comes from the left, it’s louder on that side, even if we can’t see an object there.
Anyone who has heard a siren approaching or receding has also experienced the Doppler effect, where the pitch increases or decreases depending on how fast it’s coming or going. A higher pitch can give an autonomous vehicle an immediate warning that an emergency vehicle is approaching from a blind corner (and its direction of approach), even if the lights aren’t visible on camera.
Building the models for audio perception
Edge has a general model that it develops and fine-tunes for customer needs. These models face different delay target requirements for real-time activity. Salomaki cited a sub-33-millisecond tolerance for sports broadcasting and said other environments might tolerate 100 or 200 milliseconds.
Useful perception needs to arrive on time for most applications because hearing what happens needs to be paired with seeing.
The company is building toward hardware independence with its digital twin software and AI models, allowing Edge to assemble new applications around a core technology. This way, it will become audio infrastructure that will work alongside existing microphones and cameras customers already have for their current physical AI robots, media and other systems.
With the funding, Salomaki said, Edge will complete integration with existing enterprise customers and make the platform’s components transferable across industries ahead of a Series A.
Live sports has already given Edge a commercial market as it developed and validated its perception technology, using that to help train and build its models. Its Virtual Sound Engine, which automatically captures and isolates sound from complex environments, is already deployed commercially across the industry. Customers and partners include American audio equipment manufacturer Shure Inc., the National Basketball Association, Fox Sports Media Group and the United States Tennis Association Inc.
“In order to achieve the result everybody wants in physical AI, computers need to become a little bit more human,” Salomaki said.
Image: Edge Sound Research
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