arXiv subjects

Aaron Lawson

Publications and source records attributed to Aaron Lawson.

6 recordsLinked to original sources

arXiv2026

Lessons learned from ongoing coordination between NRAO/GBO and LEO NGSO satellite constellations

With the increased demand for spectrum usage in recent years, particularly with the expansion of NGSO satellite systems for broadband internet and cellular services, radio astronomy observatories located at remote sites have been faced with the challenge of increasingly detrimental RFI exposure from ubiquitous satellite downlinks. With the help of the U.S. NSF, NRAO and the radio astronomy community have spearheaded collaboration efforts with the commercial NGSO operators to develop different coordination techniques to alleviate the impacts of these downlink signals in radio astronomy scientific observations. This paper summarizes the ongoing coordination efforts, spectrum interference avoidance schemes, and a case study on community participation of the Operational Data Sharing (ODS) system between RAS and NGSO operators. We highlight some of the essential lessons learned from these collaborations and potential application of the ODS system to other potential spectrum stakeholders.

astro-ph.IM↗

arXiv2025

ODS: A self-reporting system for radio telescopes to coexist with adaptive satellite constellations

Low Earth orbit (LEO) satellite constellations bring broadband internet and cellular service to the most remote locations on the planet. Unfortunately, many of these locations also host some of the world's best optical and radio astronomy (RA) observatories. With the number of LEO satellites expected to increase by an order of magnitude in the upcoming decade, satellite downlink radio frequency interference (RFI) is a growing concern in protected radio-quiet areas like the United States National Radio Quiet Zone. When these satellites transmit in the spectrum near protected RA bands, undesired out-of-band emission can leak into these protected bands and impact scientific observations. In this paper, we present a self-reporting system - Operational Data Sharing (ODS) - which enables mutual awareness by publishing radio telescopes' operational information to a protected database that is available to satellite operators through a representational state transfer application programming interface (REST API). Satellite operators can use the ODS data to adapt their downlink tasking algorithms in real time to avoid overwhelming sensitive RA facilities, particularly, through the novel Telescope Boresight Avoidance (TBA) technique. Preliminary results from recent experiments between the NRAO and the SpaceX Starlink teams demonstrate the effectiveness of the ODS and TBA in reducing downlink RFI in the Karl G. Jansky Very Large Array's observations in the 1990-1995 MHz and 10.7-12.7 GHz bands. This automated ODS system is beginning to be implemented by other RA facilities and could be utilized by other satellite operators in the near future.

astro-ph.IM↗

arXiv2022

Detecting Synthetic Speech Manipulation in Real Audio Recordings

Recent advances in artificial speech and audio technologies have improved the abilities of deep-fake operators to falsify media and spread malicious misinformation. Anyone with limited coding skills can use freely available speech synthesis tools to create convincing simulations of influential speakers' voices with the malicious intent to distort the original message. With the latest technology, malicious operators do not have to generate an entire audio clip; instead, they can insert a partial manipulation or a segment of synthetic speech into a genuine audio recording to change the entire context and meaning of the original message. Detecting these insertions is especially challenging because partially manipulated audio can more easily avoid synthetic speech detectors than entirely fake messages can. This paper describes a potential partial synthetic speech detection system based on the x-ResNet architecture with a probabilistic linear discriminant analysis (PLDA) backend and interleaved aware score processing. Experimental results suggest that the PLDA backend results in a 25% average error reduction among partially synthesized datasets over a non-PLDA baseline.

cs.SD↗

arXiv2022

A Discriminative Hierarchical PLDA-based Model for Spoken Language Recognition

Spoken language recognition (SLR) refers to the automatic process used to determine the language present in a speech sample. SLR is an important task in its own right, for example, as a tool to analyze or categorize large amounts of multi-lingual data. Further, it is also an essential tool for selecting downstream applications in a work flow, for example, to chose appropriate speech recognition or machine translation models. SLR systems are usually composed of two stages, one where an embedding representing the audio sample is extracted and a second one which computes the final scores for each language. In this work, we approach the SLR task as a detection problem and implement the second stage as a probabilistic linear discriminant analysis (PLDA) model. We show that discriminative training of the PLDA parameters gives large gains with respect to the usual generative training. Further, we propose a novel hierarchical approach where two PLDA models are trained, one to generate scores for clusters of highly-related languages and a second one to generate scores conditional to each cluster. The final language detection scores are computed as a combination of these two sets of scores. The complete model is trained discriminatively to optimize a cross-entropy objective. We show that this hierarchical approach consistently outperforms the non-hierarchical one for detection of highly related languages, in many cases by large margins. We train our systems on a collection of datasets including over 100 languages, and test them both on matched and mismatched conditions, showing that the gains are robust to condition mismatch.

cs.CL↗

arXiv2019

The VOiCES from a Distance Challenge 2019 Evaluation Plan

The "VOiCES from a Distance Challenge 2019" is designed to foster research in the area of speaker recognition and automatic speech recognition (ASR) with the special focus on single channel distant/far-field audio, under noisy conditions. The main objectives of this challenge are to: (i) benchmark state-of-the-art technology in the area of speaker recognition and automatic speech recognition (ASR), (ii) support the development of new ideas and technologies in speaker recognition and ASR, (iii) support new research groups entering the field of distant/far-field speech processing, and (iv) provide a new, publicly available dataset to the community that exhibits realistic distance characteristics.

eess.AS↗

arXiv2018

Voices Obscured in Complex Environmental Settings (VOICES) corpus

This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested against uncurated speech in natural conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone. This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.

cs.SD↗