Index of content:
Volume 120, Issue 2, August 2006
- BIOACOUSTICS 
120(2006); http://dx.doi.org/10.1121/1.2216899View Description Hide Description
In a previous paper acoustic radiation force on a lipid sphere in a focused Gaussian field was calculated to demonstrate the acoustic tweezer effect near the focus. The theoretical formulation was based on the situation where the sphere is centered along the beam axis. Given intensity distribution independent of the axis, it was then approximated by a cylindrical model for the sake of simplicity. Only the axial forces were considered because no lateral forces exist due to an object’s symmetry. However, it was difficult to employ the same technique to the more general case when it is off the beam axis. To overcome the limitation, in this paper the previous model is modified to compute two additional lateral forces by carrying out the projection over arbitrary incident planes to restrict the integration limits. For different sizes of the sphere, the magnitudes of the net forces in three orthogonal directions are computed. The results show that the acoustic tweezer can be realized more easily in the lateral directions than in the axial directions. Differing from the axisymmetric case, the spheres of small sizes tend to be more strongly attracted than the larger ones in the lateral directions.
120(2006); http://dx.doi.org/10.1121/1.2211488View Description Hide Description
Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, and locate and identify prey. Their echolocation signals have been characterized in detail but their communication signals are less well understood despite their widespread use during social interactions. The goal of this study was to develop an automatic classification algorithm for characterizing the communication signals of little brown bats. Sound recordings were made overnight on five individual male bats (housed separately from a large group of captive bats) for 7 nights, using a bat detector and a digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed and classified by visual observation of a call’s temporal pattern and spectral composition. Sounds were later classified using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat’s communication signals: downward frequency-modulated calls, steep frequency-modulated calls, constant frequency calls, broadband noise bursts, and broadband click trains.
120(2006); http://dx.doi.org/10.1121/1.2211547View Description Hide Description
A vertical array of five hydrophones was used to measure the acoustic field in the vertical plane of singing humpback whales. Once a singer was located, two swimmers with snorkel gear were deployed to determine the orientation of the whale and position the boat so that the array could be deployed in front of the whale at a minimum standoff distance of at least . The spacing of the hydrophones was with the deepest hydrophone deployed at a depth of . An eight-channel TASCAM recorder with a bandwidth of was used to record the hydrophone signals. The location (distance and depth) of the singer was determined by computing the time of arrival differences between the hydrophone signals. The maximum source level varied between individual units in a song, with values between 151 and . One of the purposes of this study was to estimate potential sound exposure of nearby conspecifics. The acoustic field determined by considering the relative intensity of higher frequency harmonics in the signals indicated that the sounds are projected in the horizontal direction despite the singer being canted head downward anywhere from about 25° to 90°. High-frequency harmonics extended beyond , suggesting that humpback whales may have an upper frequency limit of hearing as high as .
Artificial neural network discrimination of black-capped chickadee (Poecile atricapillus) call notes120(2006); http://dx.doi.org/10.1121/1.2211509View Description Hide Description
Artificial neural networks were trained to discriminate between two different notes from the “chick-a-dee” call of the black-capped chickadee (Poecile atricapillus). An individual note was represented as a vector of nine summary features taken from note spectrograms. A network was trained to respond to exemplar notes of one type (e.g., A notes) and to fail to respond to exemplar notes of another type (e.g., B notes). After this training, the network was presented novel notes of the two different types, as well as notes of the same two types that had been shifted upwards or downwards in frequency. The strength of the response of the network to each novel and shifted note was recorded. When network responses were plotted as a function of the degree of frequency shift, the results were very similar to those observed in birds that were trained in an analogous task [Charrier et al., J. Comp. Psychol.119(4), 371–380 (2005)]. The implications of these results to simulating behavioral studies of animal communication are discussed.
120(2006); http://dx.doi.org/10.1121/1.2211508View Description Hide Description
The hypothesis that sounds produced by odontocetes can debilitate fish was examined. The effects of simulated odontocete pulsed signals on three species of fish commonly preyed on by odontocetes were examined, exposing three individuals of each species as well as groups of four fish to a high-frequency click of a bottlenose dolphin [peak frequency (PF) , 213-dB peak-to-peak exposure level (EL)], a midfrequency click modeled after a killer whale’s signal (PF , 208-dB EL), and a low-frequency click (PF , 193-dB EL). Fish were held in a 50-cm diameter net enclosure immediately in front of a transducer where their swimming behavior, orientation, and balance were observed with two video cameras. Clicks were presented at constant rates and in graded sweeps simulating a foraging dolphin’s “terminal buzz.” No measurable change in behavior was observed in any of the fish for any signal type or pulse modulation rate, despite the fact that clicks were at or near the maximum source levels recorded for odontocetes. Based on the results, the hypothesis that acoustic signals of odontocetes alone can disorient or “stun” prey cannot be supported.