COMPARISON OF THE ACOUSTIC FEATURES OF PROSODIC BOUNDARIES IN CHINESE FORMAL AND INFORMAL SPEECH

According to Register Grammar,prosody,as an aspect of grammar,is one way to realize different registers.This study explored the differences in the acoustic features of prosodic boundaries between Chinese formal and informal speech.Results suggested that:(1) Pauses occurred more frequently and lasted longer at prosodic boundaries in formal speech,best reflected at the Prosodic Clitic level and at the Prosodic Phrase level respectively.In formal speech,pauses at Prosodic Phrase boundaries lasted significantly longer than those at Prosodic Clitic boundaries,while this difference was not significant in informal speech.The distribution of pause duration displayed greater dispersion as the prosodic level increased.(2) In informal register,Prosodic Phrase boundaries performed higher degrees of pre-lengthening than Prosodic Clitic boundaries,while this difference was not significant in formal speech.Prosodic Clitic boundaries in formal and informal speech displayed pre-lengthening and postlengthening,respectively.(3) Pre-strengthening in the intensity of prosodic words at prosodic boundaries existed at all three levels in both registers,but it was probably a weak cue to discriminate the two registers.(4) Only slight pitch reset was found at Prosodic Clitic boundaries in formal speech and at Prosodic Phrase boundaries in informal speech.

Call Characteristic Network Reveal Geographical Patterns of Call Similarity:Applying Network Analysis to Frog’s Call Research

Individual’s phenotypic traits are the results of adaptation to ecological conditions.Therefore,different selection pressures caused by heterogeneous environments may result in phenotypic difference,especially for individuals in different geographical populations.Here,we illustrated for the first time to use social network analysis(SNA)for examining whether geographical proximity predicts the similarity patterns in call characteristics among populations of an anuran species.We recorded calls from 150 male dorsal-striped opposite-fingered treefrogs(Chiromantis doriae)at 11 populations in Hainan Province and one population in Guangdong Province in China's Mainland,and we measured eight acoustic variables for each male.Mantel test didn’t show a correlation between geographical proximity and the similarity in call characteristics among populations.In addition,we failed to find correlations between a population’s eigenvector centrality and the distance to its nearest neighbor,nor between the coefficient of variation of similarity in call characteristics of a population and the average distance to all other populations.Nevertheless,three acoustic clusters were identified by the Girvan-Newman algorithm,and clustering was partially associated with geography.Furthermore,the most central populations were included in the same cluster,but the top betweenness populations were located within different clusters,suggesting that centrality populations are not necessary bridging between clusters.These results demonstrate the potential usefulness of the SNA toolbox and indicate that SNA helps to uncover the patterns that often overlooked in other analytical methods.By using SNA in frog’s call studies,researchers could further uncover the potential relationship in call characteristics between geographical populations,further reveal the effects of ecological factors on call characteristics,and probably enhance our understanding of the adaptive evolution of acoustic signals.

Feature analysis of acoustic emission sources for rail crack detection by the finite element method

The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission （AE） sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.

A rapid audio event detection method by adopting 2D-Haar acoustic super feature vector

For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.