Balanced imitation sustains song culture in zebra finches.

Songbirds acquire songs by imitation, as humans do speech. Although imitation should drive convergence within a group and divergence through drift between groups, zebra finch songs sustain high diversity within a colony, but mild variation across colonies. We investigated this phenomenon by analyzing vocal learning statistics in 160 tutor-pupil pairs from a large breeding colony. Song imitation is persistently accurate in some families, but poor in others. This is not attributed to genetic differences, as fostered pupils copied their tutors' songs as accurately or poorly as biological pupils. Rather, pupils of tutors with low song diversity make more improvisations compared to pupils of tutors with high song diversity. We suggest that a frequency dependent balanced imitation prevents extinction of rare song elements and overabundance of common ones, promoting repertoire diversity within groups, while constraining drift across groups, which together prevents the collapse of vocal culture into either complete uniformity or chaos.

Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set.

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems 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, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks.

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface-for example, the surface of the belt-becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

Automatic Wheezing Detection Based on Signal Processing of Spectrogram and Back-Propagation Neural Network.

Wheezing is a common clinical symptom in patients with obstructive pulmonary diseases such as asthma. Automatic wheezing detection offers an objective and accurate means for identifying wheezing lung sounds, helping physicians in the diagnosis, long-term auscultation, and analysis of a patient with obstructive pulmonary disease. This paper describes the design of a fast and high-performance wheeze recognition system. A wheezing detection algorithm based on the order truncate average method and a back-propagation neural network (BPNN) is proposed. Some features are extracted from processed spectra to train a BPNN, and subsequently, test samples are analyzed by the trained BPNN to determine whether they are wheezing sounds. The respiratory sounds of 58 volunteers (32 asthmatic and 26 healthy adults) were recorded for training and testing. Experimental results of a qualitative analysis of wheeze recognition showed a high sensitivity of 0.946 and a high specificity of 1.0.

Classification of the classical male singing voice using long-term average spectrum.

OBJECTIVES/HYPOTHESIS: Singing-voice classification is often considered the cornerstone of a classical singer's identity. Traditionally, classification has been a highly subjective, nonstandardized process. As a result, misclassification of the singing voice is thought to be common, especially in young singers. Long-term average spectrum (LTAS) average is an objective measurement that could be used to classify a singer's voice. The purpose of this study was to determine the relationship of LTAS with singing-voice classification. STUDY DESIGN: Descriptive between-subject study. METHODS: Nine professional classical male singers performed the "Star Spangled Banner" in a comfortable key of their choice. LTAS was calculated for the first two phrases, the remainder of the song, and the entire song. The overall LTAS averages of each sample as well as the physiological and singing ranges were compared with self-reported singing-voice classification. RESULTS: Voice classification and overall LTAS average were moderately correlated, but the strength of the correlation varied with each sample. The strongest correlation was with the entire song. Voice classification and singing range were strongly correlated. CONCLUSIONS: LTAS remains a promising tool to aid in singing-voice classification. However, how to best use LTAS in classification remains unclear because of the influence of sample length and phonetic and pitch content on LTAS.

Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations.

A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species.

Medical devices: classification of the dental sonography device and jaw tracking device. Final rule.

The Food and Drug Administration (FDA) is classifying the dental sonography device into class I, when it is used to monitor temporomandibular joint sounds, and into class II, when it is used to interpret temporomandibular joint sounds for the diagnosis of temporomandibular joint disorders and associated orofacial pain. FDA is classifying the jaw tracking device into class I, when it is used to monitor mandibular jaw positions relative to the maxilla, and into class II, when it is used to interpret mandibular jaw positions relative to the maxilla, for the diagnosis of temporomandibular joint disorders and associated orofacial pain. Elsewhere in this issue of the Federal Register, FDA is announcing the availability of a guidance document that will serve as the special control for this device. FDA is taking this action under the Federal Food, Drug, and Cosmetic Act (the act) as amended by the Medical Device Amendments of 1976 (the 1976 amendments), the Safe Medical Devices Act of 1990 (the SMDA), the Food and Drug Administration Modernization Act of 1997 (FDAMA) and the Medical Device User Fee and Modernization Act of 2002 (MDUFMA).

Patterns in the vocalizations of male harbor seals.

Comparative analyses of the roar vocalization of male harbor seals from ten sites throughout their distribution showed that vocal variation occurs at the oceanic, regional, population, and subpopulation level. Genetic barriers based on the physical distance between harbor seal populations present a likely explanation for some of the observed vocal variation. However, site-specific vocal variations were present between genetically mixed subpopulations in California. A tree-based classification analysis grouped Scottish populations together with eastern Pacific sites, rather than amongst Atlantic sites as would be expected if variation was based purely on genetics. Lastly, within the classification tree no individual vocal parameter was consistently responsible for consecutive splits between geographic sites. Combined, these factors suggest that site-specific variation influences the development of vocal structure in harbor seals and these factors may provide evidence for the occurrence of vocal dialects.

Variation in humpback whale (Megaptera novaeangliae) song length in relation to low-frequency sound broadcasts.

Humpback whale song lengths were measured from recordings made off the west coast of the island of Hawai'i in March 1998 in relation to acoustic broadcasts ("pings") from the U.S. Navy SURTASS Low Frequency Active sonar system. Generalized additive models were used to investigate the relationships between song length and time of year, time of day, and broadcast factors. There were significant seasonal and diurnal effects. The seasonal factor was associated with changes in the density of whales sighted near shore. The diurnal factor was associated with changes in surface social activity. Songs that ended within a few minutes of the most recent ping tended to be longer than songs sung during control periods. Many songs that were overlapped by pings, and songs that ended several minutes after the most recent ping, did not differ from songs sung in control periods. The longest songs were sung between 1 and 2 h after the last ping. Humpbacks responded to louder broadcasts with longer songs. The fraction of variation in song length that could be attributed to broadcast factors was low. Much of the variation in humpback song length remains unexplained.

Classification of dolphin echolocation clicks by energy and frequency distributions.

Dolphins demonstrate an adaptive control over echolocation click production, but little is known of the manner or degree with which control is exercised. Echolocation clicks (N approximately 30,000) were collected from an Atlantic bottlenose dolphin (Tursiops truncatus) performing object discrimination tasks in order to investigate differential click production. Seven categories of clicks were identified using the spectral conformation and relative position of -3 and -10 dB peaks. A counterpropagation network utilizing 16 inputs, 50 hidden units, and 8 output units was trained to classify clicks using the same spectral variables. The network classified novel clicks with 92% success. Additional echolocation clicks (N > 24,000) from two other dolphins were submitted to the network for classification. Classified echolocation clicks were analyzed for animal specific differences, changes in predominant click type within click trains, and task-related specificity. Differences in animal and task performance may influence click type and click train length.