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Advances in Sound Localization Part 3

Tham khảo tài liệu 'advances in sound localization part 3', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Localization Error Accuracy and Precision of Auditory Localization 67 Measure Name Symbol Type Definition Formula Comments Mean Error Mean Signed Error ME CE ME 1 iỊ xi -n x0 -n Mean Absolute Error Mean Unsigned Error MUE CE RE 1 n MUE n z x -nl ME MUE Me Mad Root-Mean-Squared Error RMSE CE RE 1 n 9 RMSE 1n s xi -n 2 RMSE2 ME2 SD2 Standard Deviation SD RE n SD jn 1x - xo 2 Mean Absolute Deviation MAD RE 1 n mad n s ixi-Xoi Table 2. Basic measures used to calculate localization error n denotes true location of the sound source . There is a continuing debate in the literature as to what constitutes a front-back error. Most authors define front-back errors as any estimates that cross the interaural axis Carlile et al. 1997 Wenzel 1999 . Other criteria include errors crossing the interaural axis by more than 10 Schonstein 2008 or 15 Best et al. 2009 or errors that are within a certain angle after subtracting 180 . An example of the last case is using a 20 range around the directly opposite angle position which corresponds closely to the range of typical listener uncertainty in the frontal direction e.g. Carlile et al. 1997 . The criterion proposed in this chapter is that only estimates exceeding a 150 error should be considered nominal front-back errors. This criterion is based on a comparative analysis of location estimates made in anechoic and less than optimal listening conditions. The extraction and separate analysis of front-back errors should not be confused with the process of trimming the data set to remove outliers even though they have the same effect. Front-back errors are not outliers in the sense that they simply represent extreme errors. They represent a different type of error that has a different underlying cause and as such should be treated differently. Any remaining errors exceeding 90 may be trimmed discarded or winsorized to keep the data set within the 90 range. Winsorizing is a strategy in which the extreme values are not removed from the sample