Index of content:
Volume 119, Issue 3, March 2006
- BIOACOUSTICS 
Acoustic detection and classification of microchiroptera using machine learning: Lessons learned from automatic speech recognition119(2006); http://dx.doi.org/10.1121/1.2166948View Description Hide Description
Current automatic acoustic detection and classification of microchiroptera utilize global features of individual calls (i.e., duration, bandwidth, frequency extrema), an approach that stems from expert knowledge of call sonograms. This approach parallels the acoustic phonetic paradigm of human automatic speech recognition (ASR), which relied on expert knowledge to account for variations in canonical linguistic units. ASR research eventually shifted from acoustic phonetics to machine learning, primarily because of the superior ability of machine learning to account for signal variation. To compare machine learning with conventional methods of detection and classification, nearly 3000 search-phase calls were hand labeled from recordings of five species: Pipistrellus bodenheimeri, Molossus molossus, Lasiurus borealis, L. cinereus semotus, and Tadarida brasiliensis. The hand labels were used to train two machine learning models: a Gaussian mixture model (GMM) for detection and classification and a hidden Markov model (HMM) for classification. The GMM detector produced 4% error compared to 32% error for a baseline broadband energy detector, while the GMM and HMM classifiers produced errors of compared to error for a baseline discriminant function analysis classifier. The experiments showed that machine learning algorithms produced errors an order of magnitude smaller than those for conventional methods.
Effects of nonlinear propagation, cavitation, and boiling in lesion formation by high intensity focused ultrasound in a gel phantom119(2006); http://dx.doi.org/10.1121/1.2161440View Description Hide Description
The importance of nonlinear acoustic wave propagation and ultrasound-induced cavitation in the acceleration of thermal lesion production by high intensity focused ultrasound was investigated experimentally and theoretically in a transparent protein-containing gel. A numerical model that accounted for nonlinear acoustic propagation was used to simulate experimental conditions. Various exposure regimes with equal total ultrasound energy but variable peak acoustic pressure were studied for single lesions and lesion stripes obtained by moving the transducer. Static overpressure was applied to suppress cavitation. Strong enhancement of lesion production was observed for high amplitude waves and was supported by modeling. Through overpressure experiments it was shown that both nonlinear propagation and cavitation mechanisms participate in accelerating lesion inception and growth. Using B-mode ultrasound, cavitation was observed at normal ambient pressure as weakly enhanced echogenicity in the focal region, but was not detected with overpressure. Formation of tadpole-shaped lesions, shifted toward the transducer, was always observed to be due to boiling. Boiling bubbles were visible in the gel and were evident as strongly echogenic regions in B-mode images. These experiments indicate that nonlinear propagation and cavitation accelerate heating, but no lesion displacement or distortion was observed in the absence of boiling.
119(2006); http://dx.doi.org/10.1121/1.2161827View Description Hide Description
The structure of humpback whale (Megaptera novaeangliae) songs was examined using information theory techniques. The song is an ordered sequence of individual sound elements separated by gaps of silence. Song samples were converted into sequences of discrete symbols by both human and automated classifiers. This paper analyzes the song structure in these symbol sequences using informationentropy estimators and autocorrelation estimators. Both parametric and nonparametric entropy estimators are applied to the symbol sequences representing the songs. The results provide quantitative evidence consistent with the hierarchical structure proposed for these songs by Payne and McVay [Science173, 587–597 (1971)]. Specifically, this analysis demonstrates that: (1) There is a strong structural constraint, or syntax, in the generation of the songs, and (2) the structural constraints exhibit periodicities with periods of 6–8 and 180–400 units. This implies that no empirical Markov model is capable of representing the songs’ structure. The results are robust to the choice of either human or automated song-to-symbol classifiers. In addition, the entropy estimates indicate that the maximum amount of information that could be communicated by the sequence of sounds made is less than .
119(2006); http://dx.doi.org/10.1121/1.2161434View Description Hide Description
The focus of this study was to investigate how dolphins use acoustic features in returning echolocation signals to discriminate among objects. An echolocating dolphin performed a match-to-sample task with objects that varied in size, shape, material, and texture. After the task was completed, the features of the object echoes were measured (e.g., target strength, peak frequency). The dolphin’s error patterns were examined in conjunction with the between-object variation in acoustic features to identify the acoustic features that the dolphin used to discriminate among the objects. The present study explored two hypotheses regarding the way dolphins use acoustic information in echoes: (1) use of a single feature, or (2) use of a linear combination of multiple features. The results suggested that dolphins do not use a single feature across all object sets or a linear combination of six echo features. Five features appeared to be important to the dolphin on four or more sets: the echo spectrum shape, the pattern of changes in target strength and number of highlights as a function of object orientation, and peak and center frequency. These data suggest that dolphins use multiple features and integrate information across echoes from a range of object orientations.
Elastic stiffness coefficients ( , , and ) for freshly excised and formalin-fixed myocardium from ultrasonic velocity measurements119(2006); http://dx.doi.org/10.1121/1.2168547View Description Hide Description
The goal of this study was to measure elastic stiffness coefficients of freshly excised and subsequently formalin-fixed myocardial tissue. Our approach was to measure the angle-dependent phase velocities associated with the propagation of a longitudinal ultrasonic wave in ovine myocardium using phase spectroscopy techniques and to interpret the results in the context of orthotropic and transversely isotropic models describing the elastic properties of myocardium. The phase velocity results together with density measurements were used to obtain the elastic stiffness coefficients , , and for both symmetries. The results for the elastic stiffness coefficients , , and are the same for both symmetries. Measurements for freshly excised myocardium and the same tissue after a period of formalin fixation were compared to examine the impact of fixation on the elastic stiffness coefficients.