u-BeepBeep: Low Energy Acoustic Ranging on Mobile Devices

We present u-BeepBeep: a low energy acoustic ranging ser- vice for mobile phones. -BeepBeep combines the efficacy of the basic BeepBeep ranging mechanism with a light-weight cross-correlation mechanism based on sparse approximation

Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

RECURRENCE RISK OF INFERIOR SURFACE LEUKOPLAKIA OF THE VOCAL CORDS: A RETROSPECTIVE STUDY

Background: Vocal fold leukoplakia (VFL) remains a diagnostic and therapeutic challenge despite our knowledge of its etiopathogenetic factors and the development of laryngeal visualisation. This study sought to identify lesions on the inferior surface of the vocal folds as a recurrence risk factor. Methods: This was a retrospective study with two years of data collection. The study included 37 VFL patients, who were separated into nonrecurrent and recurrent categories. Each patient's clinicopathological characteristics and surgical procedures were scrutinised. Results: 15 (40.5%) of the 37 patients exhibited residual (3) or recurrent (12) VFL. 8 of 12 (66.7%) patients with recurrence and 6 of 22 (27.3%) patients without recurrence had inferior surface lesions of the vocal fold at the time of the initial operation (P =.036). Significantly more recurrences occurred in patients with inferior surface lesions. Other evaluated factors were not associated with recurrence. Conclusion: The presence of VFL lesions on the inferior surface is a significant recurrence risk factor

Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations

While most research into speech synthesis has focused on synthesizing high-quality speech for in-dataset speakers, an equally essential yet unsolved problem is synthesizing speech for unseen speakers who are out-of-dataset with limited reference data, i.e., speaker adaptive speech synthesis. Many studies have proposed zero-shot speaker adaptive text-to-speech and voice conversion approaches aimed at this task. However, most current approaches suffer from the degradation of naturalness and speaker similarity when synthesizing speech for unseen speakers (i.e., speakers not in the training dataset) due to the poor generalizability of the model in out-of-distribution data. To address this problem, we propose GZS-TV, a generalizable zero-shot speaker adaptive text-to-speech and voice conversion model. GZS-TV introduces disentangled representation learning for both speaker embedding extraction and timbre transformation to improve model generalization and leverages the representation learning capability of the variational autoencoder to enhance the speaker encoder. Our experiments demonstrate that GZS-TV reduces performance degradation on unseen speakers and outperforms all baseline models in multiple datasets.Comment: 5 pages, 3 figures. Accepted by Interspeech 2023, Ora

AutoLV: Automatic Lecture Video Generator

We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video. Our system is primarily composed of a speech synthesis module with few-shot speaker adaptation and an adversarial learning-based talking-head generation module. It is capable of not only reducing instructors' workload but also changing the language and accent which can help the students follow the lecture more easily and enable a wider dissemination of lecture contents. Our experimental results show that the proposed model outperforms other current approaches in terms of authenticity, naturalness and accuracy. Here is a video demonstration of how our system works, and the outcomes of the evaluation and comparison: https://youtu.be/cY6TYkI0cog.Comment: 4 pages, 4 figures, ICIP 202