## 4 Experiment II: Listener-Preferred \(\text{SBR}\) for Speech-Background Mixing
### Interim Discussion  
A group of listeners with normal hearing were instructed to choose their preferred SBR for mixing in five different audio scenes. Listeners' preferred SBR depends on the accompanying background sounds. Compared to model-predicted \(\text{SBR}_{min}\), which is the minimum SBR for listeners to be able to understand all speech information, the listener-preferred SBRs are consistently higher. This suggests that when making a judgement on the optimal SBR level at which to listen, listeners go beyond intelligibility to consider other factors such as listening effort and comfort. Current OIMs are not able to account for this because they focus on intelligibility. When the background sound is noise, music, other sound effect or their combinations, an extra gain of 5.5 dB added to the model-predicted \(\text{SBR}_{min}\) gets close to the preferred listening level. For the 'News broadcasting' scene, in which speech by a male taker was artificially added as the background, a larger gain of 23 dB is required. Several participants tended to adjust the level of the background competing speech in the scene to as low as possible. According to the model prediction for this scene, intelligibility is no longer an issue when the SBR is above \(-\)7.5 dB (see Figure 6). One possibility is that listeners did not expect a news broadcast to contain prominent competing speech. Or maybe they considered the competing speech to offer little enhancement to a news broadcast. Informational masking introduced by competing speech could also be important. Unlike energetic masking to intelligibility, which is the consequence of interactions of physical signals acting to the peripheral auditory system, informational masking obstructs auditory identification and discrimination at the late stage of auditory pathway, when a sound is perceived in the presence of other similar sound(s) [16; 17; 40; 41; 42; 43; 44]. As the competing speech in the 'News broadcasting' scene is uttered in  
Figure 6: Listener-preferred (circle) SBR and predicted \(\text{SBR}_{min}\) (grey cross) for the audio scenes listed in Table 2. Values represented by ‘x’ markers are resulted from applying a constant 5.5 dB gain to the predicted \(\text{SBR}_{min}\) for each scene. Error bar indicates 95% confidence interval of the mean. the same language as the foreground speech, a considerable amount of informational masking is induced. Consequently, this requires more effort from the listener to overcome informational masking than when listening in other backgrounds, which introduce little informational masking [45]. In the meantime, the listener's attention is also more likely to be distracted by the background competing speech. Frequent attention switching between different channels may increase the cognitive load of the listener [46], potentially leading to escalated annoyance arising from the background sound. For implementation, the optimal SBR suggested by this system for a given scene can be calculated as the summation of the model-predicted \(\text{SBR}_{min}\) in this background and the additional gain \(\Delta\), which aims to compensate for other factors such as listening effort and comfort. Based on the scenes tested in this experiment, \(\Delta=5.5\) dB can be applied to background sounds which are free of competing speech. However, when the background is competing speech, a larger gain is needed--it is found 23 dB for the competing speech by a male talker here. To automate the decision making, one solution could be to perform automatic sound classification (e.g., [47; 48; 49]) to identify whether the background sound is speech. For cases where this information is known or can be predefined, the knowledge could be stored in the metadata. The system is then able to retrieve this information from the data flow to make a decision. However, if the background contains both speech and non-speech sounds, the intelligibility of the speech content belonging to the background may need to be estimated, in order to quantify its impact to the foreground speech.