Siren Detection Research Articles

High-Performance, Low Complexity Yelp Siren Detection System

Effective detection of emergency vehicle sirens is crucial for accident prevention and road safety, particularly at intersections where visual detection may be insufficient. This study addresses the challenges faced by existing siren detection systems, which often involve complex signal processing and high computational demands. A streamlined detection system is presented, designed specifically for recognizing yelp sirens, one of the most prevalent siren sounds. This approach, inspired by analog electronics, offers a cost-effective and low-complexity solution suitable for deployment in either digital or predominantly analog setups. Evaluation using a comprehensive dataset of real-world siren signals and road noise demonstrates performance comparable to state-of-the-art methods while significantly reducing computational and energy requirements. Additionally, a thorough analysis of operational parameters is included to optimize performance and cost-effectiveness. All data and methods are publicly accessible to ensure transparency and reproducibility. This work enhances road safety and accessibility for hearing-impaired drivers and provides a foundation for future research on expanding detection capabilities to other siren types.

Constraints on Coasting Cosmological Models from Gravitational-wave Standard Sirens

We present the first test of coasting cosmological models with gravitational-wave (GW) standard sirens observed in the first three observing runs of the LIGO–Virgo–KAGRA detector network. We apply the statistical galaxy catalog method adapted to coasting cosmologies and infer constraints on the H 0 Hubble constant for the three fixed values of the curvature parameter k=−1,0,+1 in H02c−2 units. The maximum posteriors and 68.3% highest density intervals we obtained from a combined analysis of 46 dark siren detections and a single bright siren detection are H0=68.1−5.6+8.5,67.5−5.2+8.3,67.1−5.8+6.6kms−1Mpc−1 , respectively. All our constraints on H 0 are consistent within 1σ with the H 0 measured with the differential age method, which provides a constraint on H 0 in coasting cosmologies independently from k. Our results constrain all cosmological models with a(t) ∝ t linear expansion in the luminosity distance and redshift range of the 47 LIGO–Virgo detections, i.e., d L ≲ 5Gpc and z ≲ 0.8, which practically include all (both strictly linear and quasi-linear) models in the coasting model family. As we have found, the coasting models and the Lambda cold dark matter (or ΛCDM) model fit equally well to the applied set of GW detections.

Few-Shot Emergency Siren Detection.

It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the best knowledge learned in associating a sound with its class representation is transferred to identify ambulance sirens, given only a few instances for the prototype computation. Performance is evaluated on siren recordings acquired by sensors inside and outside the cabin of an equipped car, investigating the contribution of filtering techniques for background noise reduction. The results show the effectiveness of the proposed approach, achieving AUPRC scores equal to 0.86 and 0.91 in unfiltered and filtered conditions, respectively, outperforming a convolutional baseline model with and without fine-tuning for domain adaptation. Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.

Development and assessment of an environmental DNA (eDNA) assay for a cryptic Siren (Amphibia: Sirenidae)

Environmental DNA (eDNA) assays have become a major aspect of surveys for aquatic organisms in the past decade. These methods are highly sensitive, making them well-suited for monitoring rare and cryptic species. Current efforts to study the Rio Grande Siren in southern Texas have been hampered due to the cryptic nature of these aquatic salamanders. Arid conditions further add to the difficulty in studying this species, as many water bodies they inhabit are ephemeral, sometimes constraining sampling efforts to a short window after heavy rain. Additionally, sirens are known to cease activity and reside underground when ponds begin to dry or as water temperatures increase. Conventional sampling efforts require extensive trap-hours to be effective, which is not always possible within the required sampling window. This study presents the development of a novel eDNA assay technique for this elusive species using conventional PCR and Sanger sequencing and compares eDNA sampling results with simultaneous trapping at multiple sites to assess the relative effectiveness of the procedure. Rio Grande Siren detection via eDNA sampling was significantly higher at all sites compared to trapping, confirming the utility of this assay for species detection. This methodology gives promise for future work assessing the distribution and status of the Rio Grande Siren and has potential for use on other southern Texas amphibians.

An interacting dark sector and the implications of the first gravitational-wave standard siren detection on current constraints

An interacting dark sector and the implications of the first gravitational-wave standard siren detection on current constraints

An Embedded Fuzzy Logic Based Application for Density Traffic Control System

The control of density traffic at cross junction road usually manned by human efforts or implementation of automatic traffic light system. This system seem and proves to be inefficient with some challenges. The major constraints of this traffic control are as a result of the inability of most traffic control systems to assign appropriate waiting time for vehicles based on the lane density. Also with little or no consideration for pedestrians, emergency and security agents priorities. In view of this, an intelligent density traffic control system using (fuzzy logic) which is capable of providing priority to the road users based on the density and emergency situations was developed and presented in this paper. This system will obtain the approximate amount of vehicle and presence of pedestrians respectfully on each lane with help of Infrared Sensors (IR) and siren detection system for emergency and security road users. The working principle of this system depending on the logic inputs rules given into the processing unit by the (sensors, S1 and S2) which helps the system to generates a timing sequence that best suit the number of vehicles and pedestrians available on the lane at point in time.

A Siren Detection System based on Mechanical Resonant Filters

A system based on mechanical resonant filters is proposed that can be used for the detection of acoustical signals the frequency components of which vary according to specific periodic patterns. Usually, signals of this category produced by the siren of an emergency vehicle. The device essentially implements a mechanical narrow filter bank that covers the frequency range of a typical siren sound. Signal detection is obtained by measuring the time delay between successive activation of the filters of the bank. The whole analysis reveals how a set of simple, low-cost mechanical resonant filters can replace an electronic analog or digital system for the implementation of a filter bank. Moreover, a scaling down procedure is proposed so that a microsystem may be developed.

System for detecting the siren of an approaching emergency vehicle

A siren detection system controls the lights at an intersection to direct traffic and permit an emergency vehicle to travel through the intersection unimpeded. The system determines the frequency of the sound emanating from a siren carried by the emergency vehicle by counting pulses that indicate the frequency of the sound, by determining the elapsed time necessary to count a selected number of pulses, and by utilizing the elapsed time and number of pulses counted to determine the frequency of sound emitted by the siren.

The Design of Motorcycle Helmet Headphones for Emergency Siren Detection

In recent years, sophisticated audio systems have been installed on touring motorcycles. One feature of these systems is the provision for headphones on or inside the rider's helmet. With such an installation, the rider must still be able to perceive safety-related sounds such as the siren of an emergency vehicle. This paper describes field and laboratory experiments used to study the rider's ability to detect and localize such warning sirens, in the presence of audio program material presented in various ways. The spectral qualities of sirens and ambient motorcycle noise are considered, as well as the sound attenuation properties of helmets. The results lead to a specification for an audio filter which attenuates the program amplitude in the spectral region of a siren. This permits the rider to hear a siren with adequate warning, yet the desired high level of sound quality can be maintained.