胡来，任性
经过近20年的探索于实践，起步于深圳的物业管理事业从无到有，从小到大，从模仿到创新。随着物业的发展，一路走来已经有了令人瞩目的成就。但是物业目前的状态还不是处于成熟阶段，还有许多需要改进和升华的地方。因此我们必须发挥我们的智慧让物业服务企业跨入新世界，将物业管理推向新的高度。目前我们需要就企业内部管理构造做出进一步的改变。有数量型转变成质量型，由全服务型转变成管理型等角色的转变。这就更加引起了我们的思考，更加需要发挥出我们的智慧，探讨国内物业管理行业，迎接这新的机遇和挑战。运用智能化的住宅管理，给物业管理一个新的发展空间，也给物业管理一个展现价值的机会。给物业管理增加新的、技术含量较高的管理服务，使物业管理真正的有了“用武之地After near 20 year explorations in the practice, starts in Shenzhen's estate management enterprise grows out of nothing, from infancy to maturity, from imitation to innovation. Along with the property development, a group walked already had the amazing achievement. But the property present condition is at the mature stage, but also many need to improve the place which and to sublimate. Therefore we must display our wisdom to let the property service enterprise stride in the new world, pushes to the new altitude the estate management. At present we need to make the further change on the enterprise internal management structure. Has the scalar type to transform the quality, by role and so on entire service transformation management transformations. This has even more caused our ponder, even more needs to display our wisdom, the discussion domestic estate management profession, greets this new opportunity and the challenge. Using intellectualized housing management, for estate management recent development opportunities, also gives an estate management development value the opportunity. Increases newly, the technique content high supervisory service for the estate management, enabled the estate management true to have “the opportunity
Greta：）杨婷
The necessity for accurate speech recognition systems capableof handling adverse environments with multiple speakershas in recent years fueled speech separation and enhancementresearch [1, 5, 3, 2, 6, 4]. This has resulted in numeroustechniques with varying degrees of success, most ofwhich employ multiple microphones [1, 3, 2, 6, 4]. Beamformingtechniques, for example, utilize knowledge aboutthe direction of the speech source of interest in order to reducenoise from other directions. The resulting SNR gainis significant as long as a large number of microphones areavailable [6, 4]. Independent Component Analysis (ICA),on the other hand, is capable of producing large SNR gainswith few microphones [1, 3]. However, ICA has severallimitations that have hampered its application in real-worldsituations [1, 6].Interestingly, both of these popular techniques (as wellas many other speech separation techniques) are similar inthe sense that they are not specifically designed to functionfor speech signals. Speech has certain characteristics whoseutilization can provide a significant edge in the de-noisingand signal separation tasks [2, 5].In this paper, we extend the phase error filtering techniqueinitially proposed in [2] to include the magnitudesof the two microphones in addition to the phase information.This technique transforms two noisy time-domain signalsrecorded by two microphones into their time-frequency(TF) representations. For each time-frequency componentor block, a phase-error measure is derived from the informationin both microphones. Based on this, the time-frequencyblock for each microphone is scaled by a masking value betweenzero and one. Basically, TF blocks with large phaseerrorsare ‘punished’ by a small mask value (0) and TFblocks with small phase-errors are ‘rewarded’ by a largemask value (1).In the following sections, we formulate four different TFmasks and analyze them theoretically, through SNR-gainsimulations, and digit recognition experiments.
