Herringbone planetary gear system (HPGS) has high power density and complex structure. The torsional flexibility of the left and right teeth of the sun gear is closely related to the dynamic characteristics of the HPGS. In this research, considering the coordination conditions of both sides torsional stiffness and axial slide of the sun gear, a new dynamic model of the HPGS considering the meshing phase difference between left and right teeth of the sun gear is developed based on the lumped-parameter method, and the influence mechanism of torsional stiffness and axial sliding is studied. Moreover, the dynamic parameters and dynamic characteristics of the HPGS are analyzed in the case of varying torsoinal stiffness and axial slide. The results show that the torsional stiffness of left and right teeth and the axial slide of sun gear have significant impacts on the dynamic parameters and dynamic mesh force response. With the increase of the torsional flexibility (the decrease the torsional stiffness), the sun gear and planet gear meshing stiffness and the maximum tooth surface load are both increased on the left side (input side) and decreased on the right side, but the main peak values and peak frequencies of dynamic response on both sides of the s-p meshing pairs decrease significantly. In addition, when the sun gear slides toward the output side axially, meshing stiffness and dynamic mesh force response main peak values decreased on the left side (input side) and increased on the right side, but the main resonance peaks frequencies keep the same.

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Audio fingerprint is a compact unique content-based digital signature of an audio signal. It's an interesting technique that can be used to identify unknown audio clips. Generally, it mainly consists of two parts, i.e. fingerprint extracting from audio signals and fingerprint matching against those stored in a fingerprint database that has been set up beforehand. With the rapid growth in the quantity of audio files, the probability of collision of different audio signals become relatively high and it has become very challenging to retrieve an audio recording in real-time from the ever-growing huge database. In this letter, we introduce a reliable audio fingerprinting system, which extracts audio fingerprints from an audio signal based on its spectral energy structure. Preliminary experimental results suggest that this fingerprinting system can work well in the application of broadcast monitoring.

No abstract

Audio fingerprint is an effective representation of an audio signal using low-level features and can be used to identify unlabeled audio based on its content. In this paper, we introduce a robust audio feature, local energy centroid (LEC), which can represent the energy conglomeration degree of the relative small region in the spectrum. Our audio fingerprint is generated based on the LEC feature which is conducive to enhance the robustness of system. In audio retrieval processing, an improved scoring strategy is proposed to resist the linear speed change. Experimental results show that the new fingerprinting system is quite robust in the present of noise and the proposed method can achieve satisfying recognition accuracy.