Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
Detection and diagnosis method is proposed for surface damage in immersed structures. It is based on noncontact ultrasonic\nechography measurements, signal processing tools, and artificial intelligence methods. Significant features are extracted from the\nmeasured signals and a classification method is developed to detect the echoes resulting from surface damage in an immersed\nstructure.The identification of the damage is also provided. Gaussian neural networks trained with a specific learning algorithm are\ndeveloped for this purpose. The performance of the method is validated by laboratory experiments which indicate that this method\ncould be suitable for the monitoring of inaccessible systems like marine turbines whose unavailability causes severe economic losses....
The present paper reviews the vibro-acoustic\nmodelling of extruded aluminium train floor structures\nincluding the state-of-the-art of its industrial applications, as\nwell as the most recent developments on mid-frequency modelling\ntechniques in general. With the common purpose to\npredict mid-frequency vibro-acoustic responses of stiffened\npanel structures to an acceptable accuracy at a reasonable\ncomputational cost, relevant techniques are mainly based on\none of the following three types of mid-frequency vibroacoustic\nmodelling principles: (1) enhanced deterministic\nmethods, (2) enhanced statistical methods, and (3) hybrid\ndeterministic/statistical methods. It is shown that, although\nrecent developments have led to a significant step forward in\nindustrial applicability, mature and adequate prediction techniques,\nhowever, are still very much required for solving sound\ntransmission through, and radiation from, extruded aluminium\npanels used on high-speed trains. Due to their great potentials\nfor predicting mid-frequency vibro-acoustics of stiffened panel\nstructures, two of recently developed mid-frequency modelling\napproaches, i.e. the so-called hybrid finite element-statistical\nenergy analysis (FE-SEA) and hybrid wave-based method statistical\nenergy analysis (WBM-SEA), are then recapitulated....
The automatic recognition of MP3 compressed speech presents a challenge to the current systems due to the lossy\nnature of compression which causes irreversible degradation of the speech wave. This article evaluates the\nperformance of a recognition system optimized for MP3 compressed speech with current state-of-the-art acoustic\nmodelling techniques and one specific front-end compensation method. The article concentrates on acoustic model\nadaptation, discriminative training, and additional dithering as prominent means of compensating for the described\ndistortion in the task of phoneme and large vocabulary continuous speech recognition (LVCSR). The experiments\npresented on the phoneme task show a dramatic increase of the recognition error for unvoiced speech units as a\ndirect result of compression. The application of acoustic model adaptation has proved to yield the highest relative\ncontribution while the gain of discriminative training diminished with decreasing bit-rate. The application of\nadditional dithering yielded a consistent improvement only for the MFCC features, but the overall results were still\nworse than those for the PLP features....
Acoustic data transmission (ADT) forms a branch of the audio data hiding techniques with its capability of\ncommunicating data in short-range aerial space between a loudspeaker and a microphone. In this paper, we propose\nan acoustic data transmission system extending our previous studies and give an in-depth analysis of its performance.\nThe proposed technique utilizes the phases of modulated complex lapped transform (MCLT) coefficients of the audio\nsignal. To achieve a good trade-off between the audio quality and the data transmission performance, the enhanced\nsegmental SNR adjustment (SSA) algorithm is proposed. Moreover, we also propose a scheme to use multiple\nmicrophones for ADT technique. This multi-microphone ADT technique further enhances the transmission\nperformance while ensuring compatibility with the single microphone system. From a series of experimental results, it\nhas been found that the transmission performance improves when the length of the MCLT frame gets longer at the\ncost of the audio quality degradation. In addition, a good trade-off between the audio quality and data transmission\nperformance is achieved by means of SSA algorithm. The experimental results also reveal that the proposed\nmulti-microphone method is useful in enhancing the transmission performance....
We investigate the automatic recognition of emotions in the singing voice and study the worth and role of a variety of\nrelevant acoustic parameters. The data set contains phrases and vocalises sung by eight renowned professional opera\nsingers in ten different emotions and a neutral state. The states are mapped to ternary arousal and valence labels. We\npropose a small set of relevant acoustic features basing on our previous findings on the same data and compare it\nwith a large-scale state-of-the-art feature set for paralinguistics recognition, the baseline feature set of the Interspeech\n2013 Computational Paralinguistics ChallengE (ComParE). A feature importance analysis with respect to classification\naccuracy and correlation of features with the targets is provided in the paper. Results show that the classification\nperformance with both feature sets is similar for arousal, while the ComParE set is superior for valence. Intra singer\nfeature ranking criteria further improve the classification accuracy in a leave-one-singer-out cross validation\nsignificantly....
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