A Phonological Analysis of Male Gibbon Singing Behavior

Behaviour ◽  
1989 ◽  
Vol 109 (1-2) ◽  
pp. 20-45 ◽  
Author(s):  
Peter Marler ◽  
John C. Mitani

AbstractAcoustic analyses and experimental field playbacks were conducted to investigate the nature and communicative significance of the phonological structure and organization of male gibbon (Hylobates agilis) songs. Males use a limited number of spectrographically discrete elements or note types to construct songs. A classification of these note types was produced initially through a visual sorting process using gross spectral and temporal features. Measurement of single acoustic variables and a digital sound program, which compared the two-dimensional cross-correlation values of note spectrograms, were employed to check the results of the qualitative sorting procedure. The sequential organization of notes composing songs was examined by tabulating the frequencies of occurrence of each note type in different positions and the transition probabilities between note types. These analyses revealed that songs are formed within a framework of rules, which define regular patterns in the placement and order of note types. To investigate whether the gibbons employ these rules in a biologically meaningful fashion, a field playback experiment was conducted comparing the responses of animals to normal and phonologically rearranged songs. Although the gibbons identify rearranged songs as conspecific territorial signals, responses to playbacks suggested that the hypothesized rules generating songs are communicatively significant; male gibbons responded in a qualitatively different fashion to normal and phonologically rearranged songs. These results are evaluated in light of other studies of animal vocal communication.

2021 ◽  
Author(s):  
Chiara De Gregorio ◽  
Filippo Carugati ◽  
Vittoria Estienne ◽  
Daria Valente ◽  
Teresa Raimondi ◽  
...  

Abstract In animal vocal communication, the development of adult-like vocalization is fundamental to interact appropriately with conspecifics. However, the factors that guide ontogenetic changes in the acoustic features remains poorly understood. In contrast with a historical view of nonhuman primate vocal production as substantially innate, recent research suggests that inheritance and physiological modification can only explain some of the developmental changes in call structure during growth. A particular case of acoustic communication is the indris' singing behavior, a peculiar case among Strepsirrhine primates. Thanks to a decade of intense data collection, this work provides the first long-term quantitative analysis on song development in a singing primate. To understand the ontogeny of such a complex vocal output, we investigated juvenile and sub-adult indris' vocal behaviour, and we found that young individuals started participating in the chorus years earlier than previously reported. Our results indicated that spectro-temporal song parameters underwent essential changes during growth. In particular, the age and sex of the emitter influenced the indris' vocal activity. We found that frequency parameters showed consistent changes across the sexes, but the temporal features showed different developmental trajectories for males and females. Given the low level of morphological sexual dimorphism and the marked differences in vocal behavior, we hypothesize that factors like social influences and auditory feedback may affect songs' features, resulting in high vocal flexibility in juvenile indris. This trait may be pivotal in a species that engages in choruses with rapid vocal turn-taking.


2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.


2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.


Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.


Heliyon ◽  
2019 ◽  
Vol 5 (8) ◽  
pp. e02046 ◽  
Author(s):  
Choug Abdelkrim ◽  
Mohamed Salah Meridjet ◽  
Nadir Boutasseta ◽  
Lakhdar Boulanouar

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80287-80299 ◽  
Author(s):  
Yu Wang ◽  
Xiaojuan Ban ◽  
Huan Wang ◽  
Di Wu ◽  
Hao Wang ◽  
...  

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Congshu Liao ◽  
Dongfeng Li

ZENK expression in vocal nuclei is associated with singing behavior. Area X is an important nucleus for learning and stabilizing birdsong. ZENK expression is higher in Area X compared to that in other vocal nuclei when birds are singing. To reveal the relationship between the ZENK expression in Area X and song crystallization, immunohistochemistry was used to detect ZENK protein expression in Area X after the unilateral vocal nerve (tracheosyringeal nerve) section in adult male zebra finches. Sham operations had no effect on song. In contrast, section of unilateral vocal nerve could induce song decrystallization at the 7th day after the surgery. The spectral and the temporal features of birdsong were distorted more significantly in the right-side vocal nerve section than in the left-side vocal nerve section. In addition, after surgery, ZENK expression was higher in the right-side of Area X than in the left-side. These results indicate that the vocal nerve innervations probably are right-side dominant. ZENK expression in both sides of Area X decreased, as compared to control group after surgery, which suggests that the ZENK expression in Area X is related to birdsong crystallization, and that there is cooperation between the Area X in AFP and syrinx nerve.


Phonology ◽  
1988 ◽  
Vol 5 (1) ◽  
pp. 157-159 ◽  
Author(s):  
John Anderson

Shattuck-Hufnagel (1986) (henceforth S-H), in discussing the significance of ‘slips of the tongue’ for a model of processing, is particularly concerned with attempting to establish to what extent some of the constructs posited by phonologists in their characterisations of phonological structure can be shown to have a role in the planning of speech production. On the basis of the ‘MIT corpus’ of errors (collected by Merrill Garrett and herself), particularly those involving vowels, she argues for a role in planning for aspects of syllable structure, placement of lexical stress, and distinctions and dimensions such as those commonly captured by feature notations. My concern here is with some further aspects of syllable structure: I shall suggest that her data provide support for even more detailed structural properties than are proposed by S-H herself; indeed, that one uncertainty in assessing the significance of the data can be resolved by an independently motivated analysis of subsyllabic structure. I shall not repeat here the details of the classification of errors on which S-H's discussion is based (1986: §1); what is significant in the present context is that errors of various types provide a motivation for particular subsyllabic units.


2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Qibin Zhao ◽  
Liqing Zhang

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.


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