Biology subjects

Picinali, L.

Publications and source records attributed to Picinali, L..

10 recordsLinked to original sources

bioRxiv2026

Physiological Markers of Auditory Situational Awareness in Complex Spatialised Scenes

Detecting changes in acoustic environments is essential for situational awareness. It remains unclear whether the spatial location of such changes modulates automatic orienting and arousal mechanisms. We measured pupil dilation, pupil dilation rate, and microsaccade rate while listeners (n=25) heard complex, spatialized auditory scenes rendered over headphones using individualized HRTFs. Participants were naive to the critical manipulation: the appearance of a new source from one of five locations: front, left, right, back, or above. A subsequent localization task assessed perceptual spatial uncertainty. Behaviorally-irrelevant source appearances elicited a cascade of ocular responses. Microsaccadic inhibition emerged from [~]85ms after change onset, and was broadly comparable across locations, suggesting a location-invariant early orienting response to auditory change. Pupil dilation rate increased from [~]200ms, followed by a phasic pupil dilation response from [~]400ms, indicating engagement of arousal-related systems. Pupil responses were modulated by source location: changes from front/left/right elicited larger dilation than changes from above, with back responses showing a similar but weaker reduction. Behavioral localization revealed substantial confusion for front/back/above locations. However, this did not mirror the physiological data, as front sources elicited pupil responses comparable to lateral sources. These findings demonstrate that complex auditory scene changes recruit oculomotor and autonomic systems even outside the focus of task relevance. They further suggest a dissociation between early, location-invariant attentional capture indexed by microsaccadic inhibition and later, location-sensitive arousal indexed by pupil dilation. Spatial biases in auditory situational awareness therefore appear to emerge after initial change detection, shaping arousal and behavioral performance rather than the earliest orienting response.

neuroscience↗

bioRxiv2025

Spatial auditory change detection in listeners with hearing loss

Everyday listening relies on the auditory systems ability to automatically monitor the background soundscape and detect new or changing sources. Although change detection is a fundamental aspect of situational awareness, little is known about how hearing impairment affects this ability. This study examined how sensorineural hearing loss influences spatial auditory change detection. Older hearing-impaired listeners (N = 30) completed a spatial change detection task requiring them to identify the appearance of a new sound source within a complex spatialised acoustic scene. Hearing loss was characterised by three factors that were measured with standard clinical tests: audiometric hearing thresholds, sensitivity to small level changes, and sensitivity to spectrotemporal modulation. Simple and mixed-effects linear models were used to test how these factors predicted reaction time, hit rate, and false alarm rate. Listeners with poorer spectrotemporal sensitivity, higher audiometric hearing thresholds, and older age showed slower and less accurate detection, whereas sensitivity to small changes in level did not predict outcomes. Detection also varied with spatial location, where appearing sources from behind were detected more slowly and less accurately than those from the front or sides. Numerical analysis using head-related transfer functions confirmed that these rear-field effects were unlikely to be explained by overall or frequency-specific acoustic level differences. These findings reveal that hearing loss, age, and spatial factors jointly shape listeners ability to monitor dynamic auditory scenes. Additionally, testing spectrotemporal sensitivity offers a promising clinical measure of non-speech auditory processing with relevance for hearing-aid fitting and situational awareness.

neuroscience↗

bioRxiv2025

Robotics-assisted acoustic surveys could deliver reliable, landscape-level biodiversity insights

Terrestrial remote sensing approaches, such as acoustic monitoring, deliver finely resolved and reliable biodiversity data. However, the scalability of surveys is often limited by the effort, time and cost needed to deploy, maintain and retrieve sensors. Autonomous unmanned aerial vehicles (UAVs, or drones) are emerging as a promising tool for fully autonomous data collection, but there is considerable scope for their further use in ecology. In this study, we explored whether a novel approach to UAV-based acoustic monitoring could detect biodiversity patterns across a varied tropical landscape in Costa Rica. We simulated surveys of UAVs employing intermittent locomotion-based sampling strategies on an existing dataset of 26,411 hours of audio recorded from 341 static sites, with automated detections of 19 bird species (n = 1,819) and spider monkey (n = 2,977) vocalisations. We varied the number of samplers deployed in a single survey (sampling intensity) and whether the samplers move between sites randomly, in a pre-determined route to minimise travel time, or by adaptively responding to real-time detections (sampling strategy) and measured the impact on downstream ecological analyses. We found that observed avian species richness and spider monkey occupancy was not impacted by sampling strategy, but that sampling intensity had a strong influence on downstream metrics. Whilst our simulated UAV surveys were effective in capturing broad biodiversity trends, such as spider monkey occupancy and avian habitat associations, they were less suited for exhaustive species inventories, with rare species often missed at low sampling intensities. As autonomous UAV systems and acoustic AI analyses become more reliable and accessible, our study shows that combining these technologies could deliver valuable biodiversity data at scale.

ecology↗

bioRxiv2024

Assessing Behavioral and Neural Correlates of Change Detection in Spatialized Acoustic Scenes

The ability to detect changes in complex auditory scenes is crucial for human survival, yet the neural mechanisms underlying this process remain elusive. This study investigates how the presence and location of sound sources impacts active auditory change detection as well as neural correlates of passive change detection. We employed stimuli designed to minimize semantic associations while preserving naturalistic temporal envelopes and broadband spectra, presented in a spatial loudspeaker array. Behavioral change detection experiments tasked participants with detecting new sources added to spatialized and non-spatialized multi-source auditory scenes. In a passive listening experiment, participants were given a visual decoy task while neural data were collected via electroencephalography (EEG) during exposure to unattended spatialized scenes and added sources. Our behavioral experiments (N = 21 and 21) demonstrated that spatializing sounds facilitated change detection compared to non-spatialized presentation, but that performance declined with increasing number of sound sources and higher hearing thresholds at high frequencies, exclusively in spatialized conditions. Slower reaction times were also observed when changes occurred from above or behind the listener, exacerbated by a higher number of sources. EEG experiments (N = 32 and 30), using the same stimuli, showed robust change-evoked responses. However, no significant differences were detected in our analysis as a function of spatial location of the appearing source.

neuroscience↗

bioRxiv2024

MAARU: Multichannel Acoustic Autonomous Recording Unit for spatial ecosystem monitoring.

1.1 - Acoustic localisation, which relies on simultaneous multi-microphone recording, adds spatial information to recorded audio and has been used in ecosystem monitoring to count individuals to improve abundance estimates, locate illegal activities such as logging/poaching, and monitor behaviour such as habitat use or species interactions. Studies have shown many advantages of acoustic localisation, but uptake remains limited as the equipment is often expensive, inaccessible, or only suitable for short-term deployments. 2 -Here, we present a low-cost, open-source, 6-channel recorder built entirely from commercially available components which can be integrated into a solar-powered, networked system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works in long-term autonomous, passive, and ad-hoc deployments. We introduce MAARUs hardware and software and present the results of lab and field tests investigating the devices durability, localisation accuracy, and other applications. 3 -MAARU provides multichannel data with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the UK and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6kHz and 65dB bird calls as far as 8m away ({+/-}10{degrees} range, 100% and >60% of signals respectively), louder calls may have even further detection radii. We also show how beamforming can be used on MAARU devices to improve species ID confidence scores by 20%+ and recall by 10%+ when using the BirdNET automated bird identification algorithms. 4 -MAARU is an accessible, low-cost option for those looking to explore spatial soundscape ecology accurately and easily. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for a new type of exploration into sonic environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/576628v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1078827org.highwire.dtl.DTLVardef@17bd5b1org.highwire.dtl.DTLVardef@10a6696org.highwire.dtl.DTLVardef@1d518f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2020

Soundscapes predict species occurrence in tropical forests

O_LIAccurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious, and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys, but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g., for silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data. C_LIO_LIWe investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual point-count dataset. C_LIO_LISoundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns. C_LIO_LISoundscapes were a better predictor of species occurrence than above-ground biomass - a metric often used to quantify habitat quality across forest degradation gradients. C_LIO_LISynthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets. C_LI

ecology↗

bioRxiv2020

SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo

O_LIAutomated monitoring approaches offer an avenue to deep, large-scale insight into how ecosystems respond to human pressures. Since sensor technology and data analyses are often treated independantly, there are no open-source examples of end-to-end, real-time ecological monitoring networks. C_LIO_LIHere, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real-time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public-facing website. C_LIO_LIWe provide the open-source code and design of our monitoring devices, the central web2py database and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real-time analyses of the eco-acoustic data. C_LIO_LIBy detailing a fully functional, open-source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology. C_LI

ecology↗

bioRxiv2019

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

ecology↗

bioRxiv2017

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

O_LIAutomated methods of monitoring ecosystems provide a cost-effective way to track changes in natural systems dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort.\nC_LIO_LIHere we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography.\nC_LIO_LIWe show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.\nC_LIO_LIWe provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.\nC_LI

ecology↗

bioRxiv2017

Effects of gamification and active listening on short-term sound localization training in virtual reality

Head-related transfer functions (HRTFs) capture the direction-dependant way that sound interacts with the head and torso. In virtual audio systems, which aim to emulate these effects, non-individualized, generic HRTFs are typically used leading to an inaccurate perception of virtual sound location. Training has the potential to exploit the brains ability to adapt to these unfamiliar cues. In this study, three virtual sound localization training paradigms were evaluated; one provided simple visual positional confirmation of sound source location, a second introduced game design elements ("gamification") and a final version additionally utilized head-tracking to provide listeners with experience of relative sound source motion ("active listening"). The results demonstrate a significant effect of training after a small number of short (12-minute) training sessions, which is retained across multiple days. Gamification alone had no significant effect on the efficacy of the training, but active listening resulted in a significantly greater improvements in localization accuracy. In general, improvements in virtual sound localization following training generalized to a second set of non-individualized HRTFs, although some HRTF-specific changes were observed in polar angle judgement for the active listening group. The implications of this on the putative mechanisms of the adaptation process are discussed.

animal behavior and cognition↗