63 histograms of Figure 58 while containing nearly as much information They are

63 histograms of figure 58 while containing nearly as

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histograms of Figure 5.8, while containing nearly as much information. They are far more informative than statistics such as standard deviation and bias, which are used frequently to evaluate performance. From Figure 5.10, it is easy to see that the data from the closest source, source 3, produced many more accurate estimates than the other two. Less than 10 percent of the estimates from source 3 had error greater than or equal to 0.5 samples (or 90 percent had error less than 0.5 samples) while about 50 percent of the others’ estimates had error greater than 4 samples. 5.2.3 RMS TDOA Error Rates with Speech Sources The experiment was run again using the array recordings of speech and the same triad array. As described in Section 3.2, there are three speech recordings in the conference-room data set, and the loudspeaker location during each was the same as it was during the Gaussian noise recordings. TDOA estimation was again performed using the same parameters: a Hanning window, 25-millisecond blocks, a 12.5-millisecond block advance, and a 0.1-sample TDOA resolution. The procedure was the same as the one applied to the Gaussian noise recordings, with one additional step. An SNR mask was used to discard any TDOAs that were produced by low-SNR speech blocks. The mask was derived using a 0.33 threshold and applied as explained in Section 3.5. Out of 399 blocks per recording, the mask passed 313 from source 1, 340 from source 2 and 297 from source 3. These speech recordings were processed using two GCC weighting functions. The first was the high-pass weighting function from Section 5.1.1, which was also applied to the Gaussian noise recordings. The second was a combination of this high-pass weighting function and the phase transform (PHAT) weighing function, which was presented in Section 4.1.2. RMS error rates were computed using the TDOA estimates produced by the speech recordings. These are shown in Figure 5.11. Also shown in this figure for comparison sake are the error rates for the corresponding Gaussian sources from Figure 5.10. Hence, for each source location, there are three error rates. The first two were derived from GCC with the high-pass weighing function, one for the Gaussian signals (labeled “Gaussian / GCC”) and one for the speech signals (labeled “Speech / GCC”). The third error rate in each plot was from GCC with the combined GCC-PHAT and high-pass weighing function for the speech signals (labeled “Speech / GCC-PHAT”). 64
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Figure 5.11 Error rates for the three source locations and two source signals at each location: Gaussian and speech. The speech sources were processed using both GCC and GCC-PHAT with SNR masks. In Figure 5.11, the Gaussian-source error rates serve as the baseline performance of GCC with the triad array in the conference room environment. These signals have properties that are nearly ideal and give GCC, in some sense, the “best you can do” results. The error rates reflect this, showing that the Gaussian sources consistently produce estimates that are more accurate than the speech sources. In general,
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