Goodness-of-fit and signal-noise ratio. Where the noise is low, the algorithm changes the clarity linearly. 

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... RR LL ii is the ii -th value of the regression line and CC is the average of the clarity curve values for the different values. An 2 of 1.0 indicates a perfect fit to the regression line. According to preliminary tests, we are using a 30 poi nt resolution on the curve which gives us the possibility to evaluate the 2 up to two decimals safely. We first evaluate the algorithm by calculating the wideband RR curve, the best fit regression line and RR 2 coefficient of determination for each RIR of 13 halls, altogether between 1621 source-receiver. The table below summarizes the results. One can see that , which means that the clarity changes almost linearly with the parameter DD RR . As mentioned earlier, by using the transition parameter, it is possible to achieve a smoo th and natural change be tween the early and the late part, while slightly lowering the good ness-of-fit. After recalculation of the numbers in Table 1 for two further transition values, we obtain slightly different, but still acceptable linear fit (see Table 2). According to the results of the wideband analysis, a single impulse response from Concert Hall 1 is selected randomly for further review. The impulse response at each density value DD RR is filtered into one-third octave bands with linear-phase group-delay compensated filters. The resulting 900 impulse response bands are analyzed by using the same method as discussed above. The results, as depicted in Fig. 7, show significant difference as compared to the wideband results shown in 3.1 (Table 1). First of all, there are outliers at certain frequencies, then the inclination of the slopes are different for each band, and less change can be introduced to the clarity by using this algorithm where there is significant noise. However, in bands with higher signal-noise ratio (SNR), the goodness-of-fit is nearly the same for all bands. Although the slopes are of different inclination, one can see that they change linearly with the algorithm in this case as well. In Fig. 8 the signal-noise ratio a nd the good ness-of-fit are shown. We calculated the signal- noise ratio of the selected RIR, based on Chu's method [7] of subtracting the root-mean-square of a given noise sample from the RIR with a threshold of 2 dB on the energy-decay curve (EDC), as defined in [1]. One can see that the SNR has significant effects on the proposed algorithm by affecting the goodness-of-fit. It is therefore proposed that the algorithm is used only on decay- corrected, noise filtered or otherwise post-processed RIRs. The objective assessment of this algorithm showed that the clarity is changing linearly, but the change in other objective parameters are not evaluated yet, which is planned as a further work. Since the algorithm does not change the late part of the RIR, it is expected that it also does not have any influence on the reverberation time, except perhaps the EDT. Other room acoustic parameters using the early parts however will likely to be affected. Although the proposed algorithm delivers a subjectively natural result without flutter or artificial sounding, however, a more detailed subjective assessment is planned to be conducted to justify this. We presented an algorithm that is capable of changing the clarity of a room impulse response (RIR). Although the resulting RIR corresponds to a room that may never exist in reality, the clarity can be changed almost linearly -- with the middle point at its original clarity value --, which is subjectively acceptable to the listener. We presented a method to evaluate the algorithm based on the calculation of the coefficient of determination, measuring the goodness-of-fit, the number representing how linearly the clarity changes with the reflection density parameter, proposed and presented in the algorithm. 1. We found that the algorithm performed very well in the case of 13 different halls between 1621 measured source-receiver positions we tested, and changed the wide-band clarity almost perfectly linearly. 2. In the case of one randomly selected impulse response that we used for narrow-band testing from the database above, we found that the clarity changed linearly for most of the bands. 3. We also found that the narrow-band goodness-of-fit was mostly dominated by the signal-noise ratio (SNR). The authors wish to thank to Mr. Márton Marschall and Mr. Ferenc Juhász for their contribution in the acoustic measurements in Hungary. This work was technically suppor ted by ENTEL Ltd., ...