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Listening to spoken content often requires modifying the speech rate while preserving the timbre and pitch of the speaker. To date, advanced signal processing techniques are used to address this task, but it still remains a challenge to maintain a high speech quality at all time-scales. Inspired by the success of speech generation using Generative Adversarial Networks (GANs), we propose a novel unsupervised learning algorithm for time-scale modification (TSM) of speech, called ScalerGAN. The model is trained using a set of speech utterances, where no time-scales are provided.

This paper presents the image source method for simulating the observed signals in the time-domain on the boundary of a spherical listening region. A wideband approach is used where all derivations are in the time-domain. The source emits a sequence of spherical wave fronts whose amplitudes could be related to the far-field directional impulse responses of a loudspeaker. Geometric methods are extensively used to model the observed signals. The spherical harmonic coefficients of the observed signals are also derived.

Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We propose a training procedure for efficient CNNs based on offline Knowledge Distillation (KD) from high-performing yet complex transformers.

Poor coordination of the speech production subsystems due to any neurological injury or a neuro-degenerative disease leads to dysarthria, a neuro-motor speech disorder. Dysarthric

This study proposes a Wave-U-Net discriminator, which is a single but expressive discriminator that assesses a waveform in a sample-wise manner with the same resolution as the input signal while extracting multilevel features via an encoder and decoder with skip connections. The experimental results demonstrate that a Wave-U-Net discriminator can be used as an alternative to a typical ensemble of discriminators while maintaining speech quality, reducing the model size, and accelerating the training speed.

Recognizing signs in virtual reality (VR) is challenging; here, we developed an American Sign Language (ASL) recognition system in a VR environment. We collected a dataset of 2,500 ASL numerical digits (0-10) and 500 instances of the ASL sign for TEA from 10 participants using an Oculus Quest 2. Participants produced ASL signs naturally, resulting in significant variability in location, orientation, duration, and motion trajectory. Additionally, the ten signers in this initial study were diverse in age, sex, ASL proficiency, and hearing status, with most being deaf lifelong ASL users.

Recognizing signs in virtual reality (VR) is challenging; here, we developed an American Sign Language (ASL) recognition system in a VR environment. We collected a dataset of 2,500 ASL numerical digits (0-10) and 500 instances of the ASL sign for TEA from 10 participants using an Oculus Quest 2. Participants produced ASL signs naturally, resulting in significant variability in location, orientation, duration, and motion trajectory. Additionally, the ten signers in this initial study were diverse in age, sex, ASL proficiency, and hearing status, with most being deaf lifelong ASL users.

The on-going paradigm shift knocking on the door of future wireless communication system is ubiquitous Internet of Things (IoT), and the maturity of which will be hindered by the challenges related to security. Artificial intelligence (AI) is proficient in solving intractable optimization problems in a data-based way, which provides a new idea for network security and physical-layer guarantee. In this paper, we divide the ubiquitous IoT networks into cyberspace and electromagnetic space, and identify the threat models.