Sorry, you need to enable JavaScript to visit this website.

In recent work [1], we developed a distributed stochastic multi-arm contextual bandit algorithm to learn optimal actions when the contexts are unknown, and M agents work collaboratively under the coordination of a central server to minimize the total regret. In our model, the agents observe only the context distribution and the exact context is unknown to the agents. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism.

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression.

In recent work [1], we developed a distributed stochastic multi-arm contextual bandit algorithm to learn optimal actions when the contexts are unknown, and M agents work collaboratively under the coordination of a central server to minimize the total regret. In our model, the agents observe only the context distribution and the exact context is unknown to the agents. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism.

High-quality speech synthesis models may be used to spread misinformation or impersonate voices. Audio watermarking can help combat such misuses by embedding a traceable signature in generated audio. However, existing audio watermarks are not designed for synthetic speech and typically demonstrate robustness to only a small set of transformations of the watermarked audio. To address this, we propose MaskMark, a neural network-based digital audio watermarking technique optimized for speech.

A speech emotion recognition (SER) system deployed on a real-world application can encounter speech contaminated with unconstrained background noise. To deal with this issue,

Although deep learning (DL) based end-to-end models have shown outstanding performance in multi-channel speech extraction, their practical applications on edge devices are restricted due to their high computational complexity. In this paper, we propose a hybrid system that can more effectively integrate the generalized sidelobe canceller (GSC) and a lightweight post-filtering model under the assistance of spatial speaker activity information provided by a directional voice activity detection (DVAD) module.

This paper proposes two novel variants of neural reprogramming to enhance wake word recognition in streaming end-to-end ASR models without updating model weights. The first, "trigger-frame reprogramming", prepends the input speech feature sequence with the learned trigger-frames of the target wake word to adjust ASR model’s hidden states for improved wake word recognition. The second, "predictor-state initialization", trains only the initial state vectors (cell and hidden states) of the LSTMs in the prediction network.

Image colorization is an ill-posed task, as objects within grayscale images can correspond to multiple colors, motivating researchers to establish a one-to-many relationship between objects and colors. Previous work mostly could only create an insufficient deterministic relationship. Normalizing flow can fully capture the color diversity from natural image manifold. However, classical flow often overlooks the color correlations between different objects, resulting in generating unrealistic color.

Large self-supervised models have excelled in various speech processing tasks, but their deployment on resource-limited devices is often impractical due to their substantial memory footprint. Previous studies have demonstrated the effectiveness of self-supervised pre-training for keyword spotting, even with constrained model capacity.

With recent advancements in generating synthetic speech, tools to generate high-quality synthetic speech impersonating any human speaker are easily available. Several incidents report misuse of high-quality synthetic speech for spreading misinformation and for large-scale financial frauds. Many methods have been proposed for detecting synthetic speech; however, there is limited work on localizing the synthetic segments within the speech signal. In this work, our goal is to localize the synthetic speech segments in a partially synthetic speech signal.