By Wolfgang Herbordt
With a consistently expanding hope for average and comfy human/machine interplay, the acoustic interface of any terminal for multimedia or telecommunication companies is challenged to permit seamless and hands-free audio communique. Sound catch for Human-Machine Interfaces introduces the sensible points of microphone array sign processing and offers quite a few mixtures of beamforming and acoustic echo cancellation.
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Extra resources for Sound Capture for Human Machine Interfaces: Practical Aspects of Microphone Array Signal Processing
Finally, the inﬂuence of the beamformer properties on the array geometry is discussed in Sect. 4. 1 Concept of Beamforming The sensor signals xm (k) are obtained by spatial and temporal sampling of the propagating wave ﬁelds by a sensor array. 2 Space-Time Filtering with Sensor Arrays 49 focusses the array on the desired source in order to separate signals from diﬀerent directions, which generally have overlapping frequency content. In our scenario, the desired signal should be extracted while interference-plusnoise should be suppressed.
The relation with MMSE optimization for wide-sense ergodic signals in the DTFT domain is shown. In Sect. 2, we illustrate typical applications of optimum linear ﬁlters as they are discussed in this work. The realization using adaptive algorithms is discussed in Chap. 7. , [Ste73, LH74, GvL89, Hay96, MIK00]. Compared to the existing literature, where generally singleinput single-output (SISO) or multiple-input single-output (MISO) systems are discussed, we formally extend the description of optimum linear ﬁlters to the MIMO case based on [BBK03].
Common zeroes do not need to be modeled by W(k), and, thus, can be eliminated before applying the described method. Especially, for G (k) = G(k), the modeling ﬁlters simplify to unit impulses. The desired response of the system G (k) between input p and output p is denoted by the column vector gp ,p (k) of length Ng , [G (k)]p ,p = gp ,p (k) . 38) where G (k) is of size Ng P × P . 39) , .. . . . . 0 · · · gp ,m (k) ˜ and captured in an (Ng + N − 1)P × M N matrix G(k) as ˜ [G(k)] p ,m = Gp ,m (k) .