Complex Event Processing for Self-Optimizing Cellular Networks.

In a cellular network, signaling and data messages exchanged between network elements are an extremely valuable information for network optimization. The consideration of different types of information allows to improve the optimization results. However, the huge amount of information has made it very difficult for operators to process all the available information. To cope with this issue, in this paper, a methodology for processing cell and user connection traces to optimize a live cellular network is presented. The aim is to generate new performance indicators different from those supplied by manufacturers, taking advantage of the ability of complex event processing tools to correlate events of different nature. For illustrative purposes, an example of how a new performance indicator is created from real traces by complex event processing is given.

Analysis of Spanish consonant recognition in 8-talker babble.

This paper presents the results of a closed-set recognition task for 80 Spanish consonant-vowel sounds (16 C × 5 V, spoken by 2 talkers) in 8-talker babble (-6, -2, +2 dB). A ranking of resistance to noise was obtained using the signal detection d' measure, and confusion patterns were analyzed using a graphical method (confusion graphs). The resulting ranking indicated the existence of three resistance groups: (1) high resistance: /ʧ, s, ʝ/; (2) mid resistance: /r, l, m, n/; and (3) low resistance: /t, θ, x, É¡, b, d, k, f, p/. Confusions involved mostly place of articulation and voicing errors, and occurred especially among consonants in the same resistance group. Three perceptual confusion groups were identified: the three low-energy fricatives (i.e., /f, θ, x/), the six stops (i.e., /p, t, k, b, d, É¡/), and three consonants with clear formant structure (i.e., /m, n, l/). The factors underlying consonant resistance and confusion patterns are discussed. The results are compared with data from other languages.