Ubeam investing

Another problem with Spatial Audio is that it can only support headphones or earphones, not speakers, so it has no benefit for people who tend to listen to music in their homes and cars. So how does our system achieve realistic soundstage audio? We start by using machine-learning software to separate the audio into multiple isolated tracks, each representing one instrument or singer or one group of instruments or singers. This separation process is called upmixing.

A producer or even a listener with no special training can then recombine the multiple tracks to re-create and personalize a desired sound field. Consider a song featuring a quartet consisting of guitar, bass, drums, and vocals. The graphical user interface displays a shape representing the stage, upon which are overlaid icons indicating the sound sources—vocals, drums, bass, guitars, and so on.

The listener can touch and drag the head icon around to change the sound field according to their own preference. Moving the head icon closer to the drums makes the sound of the drums more prominent. If the listener moves the head icon onto an icon representing an instrument or a singer, the listener will hear that performer as a solo.

The converted soundstage audio can be in two channels, if it is meant to be heard through headphones or an ordinary left- and right-channel system. Or it can be multichannel, if it is destined for playback on a multiple-speaker system. In this latter case, a soundstage audio field can be created by two, four, or more speakers. The number of distinct sound sources in the re-created sound field can even be greater than the number of speakers.

An Audio Taxonomy For a listener seeking a high degree of spatial realism, a variety of audio formats and systems are now available for enjoyment through speakers or headphones. On the low end, ordinary mono and stereo recordings provide a minimal spatial-perceptual experience.

In the middle range, multichannel recordings, such as 5. At the highest levels are audio systems that start with the individual, separated instrumental tracks of a recording and recombine them, using audio techniques and tools such as head-related transfer functions, to provide a highly realistic spatial experience. This multichannel approach should not be confused with ordinary 5. The multiple loudspeakers create a sound field that is more immersive than a standard two-speaker stereo setup, but they still fall short of the realism possible with a true soundstage recording.

When played through such a multichannel setup, our 3D Soundstage recordings bypass the 5. A word about these standards. In order to better handle the data for improved surround-sound and immersive-audio applications, new standards have been developed recently. These new standards succeed various multichannel audio formats and their corresponding coding algorithms, such as Dolby Digital AC-3 and DTS, which were developed decades ago. While developing the new standards, the experts had to take into account many different requirements and desired features.

People want to interact with the music, for example by altering the relative volumes of different instrument groups. They want to stream different kinds of multimedia, over different kinds of networks, and through different speaker configurations. SAOC was designed with these features in mind, allowing audio files to be efficiently stored and transported, while preserving the possibility for a listener to adjust the mix based on their personal taste.

To do so, however, it depends on a variety of standardized coding techniques. To create the files, SAOC uses an encoder. The inputs to the encoder are data files containing sound tracks; each track is a file representing one or more instruments. The encoder essentially compresses the data files, using standardized techniques. During playback, a decoder in your audio system decodes the files, which are then converted back to the multichannel analog sound signals by digital-to-analog converters.

Our 3D Soundstage technology bypasses this. We use mono or stereo or multichannel audio data files as input. We use AI technology to avoid multitrack rerecording, encoding, and decoding. In fact, one of the biggest technical challenges we faced in creating the 3D Soundstage system was writing that machine-learning software that separates or upmixes a conventional mono, stereo, or multichannel recording into multiple isolated tracks in real time.

The software runs on a neural network. We developed this approach for music separation in and described it in patents that were awarded in and the U. A typical session has two components: training and upmixing. In the training session, a large collection of mixed songs, along with their isolated instrument and vocal tracks, are used as the input and target output, respectively, for the neural network.

The training uses machine learning to optimize the neural-network parameters so that the output of the neural network—the collection of individual tracks of isolated instrument and vocal data—matches the target output. A neural network is very loosely modeled on the brain. In our system, the data fed to the input nodes is the data of a mixed audio track.

As this data proceeds through layers of hidden nodes, each node performs computations that produce a sum of weighted values. Then a nonlinear mathematical operation is performed on this sum. This calculation determines whether and how the audio data from that node is passed on to the nodes in the next layer. There are dozens of these layers. As the audio data goes from layer to layer, the individual instruments are gradually separated from one another.

At the end, in the output layer, each separated audio track is output on a node in the output layer. While the neural network is being trained, the output may be off the mark. It might not be an isolated instrumental track—it might contain audio elements of two instruments, for example.

In that case, the individual weights in the weighting scheme used to determine how the data passes from hidden node to hidden node are tweaked and the training is run again. This iterative training and tweaking goes on until the output matches, more or less perfectly, the target output. As with any training data set for machine learning, the greater the number of available training samples, the more effective the training will ultimately be.

In our case, we needed tens of thousands of songs and their separated instrumental tracks for training; thus, the total training music data sets were in the thousands of hours. After the neural network is trained, given a song with mixed sounds as input, the system outputs the multiple separated tracks by running them through the neural network using the system established during training.

Unmixing Audio With a Neural Network To separate a piece of music into its component tracks, 3D Soundstage relies on deep-learning software running on a neural network. The tracks are gradually separated as the digital music file progresses through successive layers of nodes.

Finally, each of the isolated tracks are released on an output node. After separating a recording into its component tracks, the next step is to remix them into a soundstage recording. This is accomplished by a soundstage signal processor. This soundstage processor performs a complex computational function to generate the output signals that drive the speakers and produce the soundstage audio.

The inputs to the generator include the isolated tracks, the physical locations of the speakers, and the desired locations of the listener and sound sources in the re-created sound field. Reuters Back in , a little startup called uBeam got a ton of attention.

It claimed that it was going to revolutionize wireless charging. And then it kind of disappeared. Everyone kind of forgot about it. But then this past Wednesday, uBeam resurfaced with an update and the assurance that they are one step closer to bringing a wireless charging technology to market. After three years of hard work, uBeam finally has a fully functional prototype and is working towards launching a consumer product in about two years' time.

The system requires both a charger, which can be inconspicuously attached to a wall, and a receiver that needs to be attached to one of your portable electronic devices. The idea for uBeam came when Meredith Perry found herself in a class at the University of Pennsylvania with a dead laptop and no charger. She began working on a wireless charging technology and decided to work on it full-time after graduating from college. That was back in

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The whole thing has sparked a big discussion on Hacker News, Tumblr and elsewhere on if venture capitalists really have the technical chops to recognize true scientific breakthroughs from ideas that are fundamentally flawed. So he lined up experts to help him investigate the company and its tech before investing. He wrote: Story continues Did the physics actually work?

Check Was it safe? Well … for starters it is just an inaudible soundwave being transferred — as in the kind also used for women during pregnancy. It also happens to be how your car likely tells the distance to objects when you park or if you have a side assist whether you can change lanes safely. Check The proof will be in the product. Perry has created a prototype that the Valley has been buzzing about for years. You are free to roam about the room with your device, even as it charges.

Other folks in the tech industry, also physicists, are already disputing Danny's post. It's mind-boggling," one of them tweeted. The whole thing has sparked a big discussion on Hacker News, Tumblr and elsewhere on if venture capitalists really have the technical chops to recognize true scientific breakthroughs from ideas that are fundamentally flawed. Advertisement uBeam was the "largest A-round check" Suster ever wrote, he said.

So he lined up experts to help him investigate the company and its tech before investing. He wrote: Did the physics actually work? Check Was it safe? Well … for starters it is just an inaudible soundwave being transferred - as in the kind also used for women during pregnancy.

It also happens to be how your car likely tells the distance to objects when you park or if you have a side assist whether you can change lanes safely. Check The proof will be in the product.