Recent events of drones flying over city centers, official buildings and nuclear installations stressed the growing threat of uncontrolled drone proliferation and the lack of real countermeasure. Indeed, detecting and tracking them can be difficult with traditional techniques. A system to acoustically detect and track small moving objects, such as drones or ground robots, using acoustic cameras is presented. The described sensor, is completely passive, and composed of a 120-element microphone array and a video camera. The acoustic imaging algorithm determines in real-time the sound power level coming from all directions, using the phase of the sound signals. A tracking algorithm is then able to follow the sound sources. Additionally, a beamforming algorithm selectively extracts the sound coming from each tracked sound source. This extracted sound signal can be used to identify sound signatures and determine the type of object. The described techniques can detect and track any object that produces noise (engines, propellers, tires, etc). It is a good complementary approach to more traditional techniques such as (i) optical and infrared cameras, for which the object may only represent few pixels and may be hidden by the blooming of a bright background, and (ii) radar or other echo-localization techniques, suffering from the weakness of the echo signal coming back to the sensor. The distance of detection depends on the type (frequency range) and volume of the noise emitted by the object, and on the background noise of the environment. Detection range and resilience to background noise were tested in both, laboratory environments and outdoor conditions. It was determined that drones can be tracked up to 160 to 250 meters, depending on their type. Speech extraction was also experimentally investigated: the speech signal of a person being 80 to 100 meters away can be captured with acceptable speech intelligibility.

Abstract:Aquatic river habitat types have been characterized and classified for over five decades based on hydrogeomorphic and ecological variables. However, few studies considered the generation of underwater sound as a unique property of aquatic habitats, and therefore as a potential information source for freshwater organisms. In this study, five common habitat types along 12 rivers in Switzerland (six replicates per habitat type) were acoustically compared. Acoustic signals were recorded by submerging two parallel hydrophones and were analysed by calculating the energetic mean as well as the temporal variance of ten octave bands (31Ð5 Hz-16 kHz). Concurrently, each habitat type was characterized by hydraulic and geomorphic variables, respectively. The average relative roughness, velocity-to-depth ratio, and Froude number explained most of the variance of the acoustic signals created in different habitat types. The average relative roughness predominantly affected middle frequencies (63 Hz-1 kHz), while streambed sediment transport increased high-frequency sound pressure levels (2-16 kHz) as well as the temporal variability of the recorded signal. Each aquatic habitat type exhibited a distinct acoustic signature or soundscape. These soundscapes may be a crucial information source for many freshwater organisms about their riverine environment.

Current literature suggests that wind turbine noise is more annoying than transportation noise. To date, however, it is not known which acoustic characteristics of wind turbines alone, i.e., without effect modifiers such as visibility, are associated with annoyance. The objective of this study was therefore to investigate and compare the short-term noise annoyance reactions to wind turbines and road traffic in controlled laboratory listening tests. A set of acoustic scenarios was created which, combined with the factorial design of the listening tests, allowed separating the individual associations of three acoustic characteristics with annoyance, namely, source type (wind turbine, road traffic), A-weighted sound pressure level, and amplitude modulation (without, periodic, random). Sixty participants rated their annoyance to the sounds. At the same A-weighted sound pressure level, wind turbine noise was found to be associated with higher annoyance than road traffic noise, particularly with amplitude modulation. The increased annoyance to amplitude modulation of wind turbines is not related to its periodicity, but seems to depend on the modulation frequency range. The study discloses a direct link of different acoustic characteristics to annoyance, yet the generalizability to long-term exposure in the field still needs to be verified.

The hydrogeomorphology and ecology of rivers and streams has been subject of intensive research for many decades. However, hydraulically-generated acoustics have been mostly neglected, even though this physical attribute is a robust signal in fluvial ecosystems. Physical generated underwater sound can be used to quantify hydro-geomorphic processes, to differentiate among aquatic habitat types, and it has implications on the behavior of organisms. In this study, acoustic signals were quantified in a flume by varying hydrogeomorphic drivers and the related turbulence and bubble formation. The acoustic signals were recorded using two hydrophones and analyzed using a signal processing software, over 31 third-octave bands (20 Hz-20 kHz), and then combined in 10 octave bands. The analytical method allowed for a major improvement of the signal-to-noise ratio, therefore greatly reducing the uncertainty in our analyses. Water velocity, relative submergence, and flow obstructions were manipulated in the flume and the resultant acoustic signals recorded. Increasing relative submergence ratio and water velocity were important for reaching a turbulence threshold above which distinct sound levels were generated. Increases in water velocity resulted in increased sound levels over a wide range of frequencies. The increases in sound levels due to relative submergence of obstacles were most pronounced in midrange frequencies (125 Hz-2 kHz). Flow obstructions in running waters created turbulence and air bubble formation, which again produced specific sound signatures.

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