Lifeboat Foundation

In principle, any pitch-shifting technique may be employed, provided that the frequency-dependent parameters analysed from the ultrasonic sound-field are mapped correctly to the frequency scale of the pitch-shifted signal. Since the spatial parameters are averaged over frequency in the currently employed configuration of the device, the frequency mapping is not required in this case. Instead, each time frame of the pitch shifted signal is spatialised according to a frequency-averaged direction. The pitch-shifting technique used for the application targeted in this article should be capable of large pitch-shifting ratios, while also operating within an acceptable latency. Based on these requirements, the phase-vocoder approach^15,16 was selected for the real-time rendering in this study, due to its low processing latency and acceptable signal quality with large pitch-shifting ratios. However, the application of other pitch-shifting methods is also demonstrated with recordings processed off-line and described in the Results section.

In summary, the proposed processing approach permits frequency-modified signals to be synthesised with plausible binaural and monaural cues, which may subsequently be delivered to the listener to enable the localisation of ultrasonic sound sources. Furthermore, since the super-hearing device turns with the head of the listener, and the processing latency of the device was constrained to 44 ms, the dynamic cues should also be preserved. Note that the effect of processing latency has been previously studied in the context of head-tracked binaural reproduction systems, where it has been found that a system latency above 50–100 ms can impair the spatial perception^17,18. Therefore, it should be noted that a trade-off must be made between: attaining high spatial image and timbral quality (which are improved through longer temporal windows and a higher level of overlapping) and having low processing latency (which relies on shorter windows and reduced overlapping). The current processing latency has been engineered so that both the spatial image and audio quality after pitch-shifting, as determined based on informal listening, remain reasonably high.

One additional advantage of the proposed approach is that only a single signal is pitch shifted, which is inherently more computationally efficient than pitch-shifting multiple signals; as would be required by the three alternative suggestions described in the Introduction section. Furthermore, the imprinting of the spatial information onto the signal only after pitch-shifting, ensures that the directional cues reproduced for the listener are not distorted by the pitch-shifting operation. The requirements for the size of microphone array are also less stringent compared to the requirements for an Ambisonics-based system. In this work, an array with a diameter of 11 mm was employed, which has a spatial aliasing frequency of approximately 17 kHz. This therefore prohibits the use of Ambisonics for the ultrasonic frequencies with the present array. By contrast, the employed spatial parameter analysis can be conducted above the spatial aliasing frequency; provided that the geometry of the array is known and that the sensors are arranged uniformly on the sphere.

When you put these three factors together—the bounty of technological advances, the compressed restructuring timetable due to covid-19, and an economy finally running at full capacity—the ingredients are in place for a productivity boom. This will not only boost living standards directly, but also frees up resources for a more ambitious policy agenda.

AI and other digital technologies have been surprisingly slow to improve economic growth. But that could be about to change.

Researchers at Oxford University have developed an AI-enabled system that can comprehensively identify people in videos by conducting detective-like, multi-domain investigations as to who they might be, from context, and from a variety of publicly available secondary sources, including the matching of audio sources with visual material from the internet.

Though the research centers on the identification of public figures, such as people appearing in television programs and films, the principle of inferring identity from context is theoretically applicable to anyone whose face, voice, or name appears in online sources.

Indeed, the paper’s own definition of fame is not limited to show business workers, with the researchers declaring ‘We denote people with many images of themselves online as famous‘.

WASHINGTON—The Biden administration launched an initiative Thursday aiming to make more government data available to artificial intelligence researchers, part of a broader push to keep the U.S. on the cutting edge of the crucial new technology.

The National Artificial Intelligence Research Resource Task Force, a group of 12 members from academia, government, and industry led by officials from the White House Office of Science and Technology Policy and the National Science Foundation, will draft a strategy for creating an AI research resource that could, in part, give researchers secure access to stores of anonymous data about Americans, from demographics to health and driving habits.

They would also look to make available computing power to analyze the data, with the goal of allowing access to researchers across the country.

Now if we could just somehow convince the Chinese to come up with 300 billion to counter this.

Beijing says bill seeks to exaggerate ‘so-called China threat’ and is ‘full of cold war thinking’

(Bloomberg) — On a Wednesday afternoon in May, an Uber driver in San Francisco was about to run out of charge on his Nissan Leaf. Normally this would mean finding a place to plug in and wait for a half hour, at least. But this Leaf was different.

Instead of plugging in, the driver pulled into a swapping station near Mission Bay, where a set of robot arms lifted the car off of the ground, unloaded the depleted batteries and replaced them with a fully charged set. Twelve minutes later the Leaf pulled away with 32 kilowatt hours of energy, enough to drive about 130 miles, for a cost of $13.

A swap like this is a rare event in the U.S. The Leaf’s replaceable battery is made by Ample, one of the only companies offering a service that’s more popular in markets in Asia. In March, Ample announced that it had deployed five stations around the Bay Area. Nearly 100 Uber drivers are using them, the company says, making an average of 1.3 swaps per day. Ample’s operation is tiny compared to the 100000 public EV chargers in the U.S.—not to mention the 150000 gas stations running more than a million nozzles. Yet Ample’s founders Khaled Hassounah and John de Souza are convinced that it’s only a matter of time before the U.S. discovers that swapping is a necessary part of the transition to electric vehicles.

COVID 19 pandemic, automation and 6G could end the metropolitan era from building high sky scrapers for companies. Companies can operate like a network from home to home without going to office. This will help a lot to bring down Urban Heat Islands and make our cities more efficient in transportation and communication to send the data even faster.

Tom Marzetta is the director of NYU Wireless, New York University’s research center for cutting-edge wireless technologies. Prior to joining NYU Wireless, Marzetta was at Nokia Bell Labs, where he developed massive MIMO. Massive MIMO (short for “multiple-input multiple-output”) allows engineers to pack dozens of small antennas into a single array. The high number of antennas means more signals can be sent and received at once, dramatically boosting a single cell tower’s efficiency.

Massive MIMO is becoming an integral part of 5G, as is an independent development that came out of NYU Wireless by the center’s founding director Ted Rappaport: Millimeter waves. And now the professors and students at NYU Wireless are already looking ahead to 6G and beyond.

Continue reading “Heres What 6G Will Be, According to the Creator of Massive MIMO” »

Colossal arc of distant galaxies makes astronomers question some of their most cherished principles about the cosmos.

Featureless “cost functions” prevent quantum machine learning algorithms from reconstructing scrambled information.