We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a single-stage, non-autoregressive transformer. During training, we predict spans of masked tokens obtained from a masking scheduler, while during inference we gradually construct the output sequence using several decoding steps. To further enhance the quality of the generated audio, we introduce a novel rescoring method in which, we leverage an external pre-trained model to rescore and rank predictions from MAGNeT, which will be then used for later decoding steps. Lastly, we explore a hybrid version of MAGNeT, in which we fuse between autoregressive and non-autoregressive models to generate the first few seconds in an autoregressive manner while the rest of the sequence is being decoded in parallel. We demonstrate the efficiency of MAGNeT for the task of text-to-music and text-to-audio generation and conduct an extensive empirical evaluation, considering both objective metrics and human studies. The proposed approach is comparable to the evaluated baselines, while being significantly faster (x$7$ faster than the autoregressive baseline). Through ablation studies and analysis, we shed light on the importance of each of the components comprising MAGNeT, together with pointing to the trade-offs between autoregressive and non-autoregressive modeling, considering latency, throughput, and generation quality. Samples are available on our demo page https://pages.cs.huji.ac.il/adiyoss-lab/MAGNeT.

Beacons are small devices which are playing an important role in the Internet of Things (IoT), connecting “things” without IP connection to the Internet via Bluetooth Low Energy (BLE) communication. In this paper we present the first private end-to-end encryption protocol called the Eddystone-Ephemeral-ID (Eddystone-EID) protocol. This protocol enables connectivity from any beacon to its remote owner, while supporting beacon’s privacy and security, and essentially preserving the beacon’s low power consumption. We describe the Eddystone-EID development goals, discuss the design decisions, show the cryptographic solution, and analyse its privacy, security, and performance. Finally, we present three secure IoT applications built on Eddystone-EID, demonstrating its utility as a security and privacy infrastructure in the IoT domain. Further, Eddystone-EID is a prototypical example of security design for an asymmetric system in which on one side there are small power-deficient elements (the beacons) and on the other side there is a powerful computing engine (a cloud). The crux of the design strategy is based on: (1) transferring work from the beacon to the cloud, and then (2) building a trade-off between cloud online work against cloud offline work, in order to enable fast real-time reaction of the cloud. These two principles seem to be generic and can be used for other problems in the IoT domain.