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Loihi 2 is a fully event-based neuromorphic processor that supports a wide range of synaptic connectivity configurations and temporal neuron dynamics. Loihi 2's temporal and event-based paradigm is naturally well-suited to processing data from an event-based sensor, such as a Dynamic Vision Sensor (DVS) or a Silicon Cochlea. However, this begs the question: How general are signal processing efficiency gains on Loihi 2 versus conventional computer architectures?

Early identification of mild cognitive impairment (MCI) is crucial for the prevention of Alzheimer’s disease. As neurodegenerative
diseases progress, the synchronous activity observed in electroencephalography (EEG) - which indicates functional connectivity -

In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text. In real-world scenarios, prior to input into an LLM, an automated speech recognition (ASR) system generates an output transcript hypothesis, where inherent errors can degrade subsequent SLU tasks.

The decaying sound field in rooms is typically described by energy decay functions (EDFs). Late reverberation can deviate considerably from the ideal diffuse field, for example, in multiple connected rooms or non-uniform absorption material distributions. This paper proposes the common-slope model of late reverberation. The model describes spatial and directional late reverberation as linear combinations of exponential decays called common slopes.

We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning. Compared to a single-task model, VoxtLM exhibits a significant improvement in speech synthesis, with improvements in both speech intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90.

We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning. Compared to a single-task model, VoxtLM exhibits a significant improvement in speech synthesis, with improvements in both speech intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90.

We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning. Compared to a single-task model, VoxtLM exhibits a significant improvement in speech synthesis, with improvements in both speech intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90.

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. They showed little to no reduction in performance while achieving large reductions in GPU memory consumption.

Covariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known.

In this paper we revisit a recently proposed underlay communication scheme which relies on repetition of the secondary signal at the transmitter and canonical correlation analysis CCA at the (multi-antenna) receiver. In this setting, CCA can provably extract the underlay signal in the presence of potentially strong and time-varying primary interference, without any channel knowledge.