# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 4 An Objective Model for Coded Spatial Audio QoE Prediction
### AMBIQUAL Design Considerations  
Humans use the differences in the acoustic signals reaching the two ears to localize sound sources. The work by [PERSON] [34] concluded that there were two cues for determining location: Interaural Time Difference (ITD), which provides information for low-frequency stimuli, and the Interaural Level Difference (ILD), which provides location information at high frequencies. According to [PERSON]'s duplex theory the ITD is used at low frequencies (below 1500 Hz), where phase difference is unambiguous, and the ILD is used at high frequencies, where the \"head shadow\" effect results in large amplitude differences. It has also been shown in recent research that envelope as well as fine structure cues can be used by the auditory system to capture ITD. This was shown to be useful in localization models for binaural signals [35]. Observations from lateralization experiments lead to a conclusion that ITD is the cue used to locate any sound with low frequencies or any high-frequency complex sound with a low-frequency repetition in the time-domain waveform [36]. Sounds not arising directly from in front (or behind) arrive earlier at one ear than at the other, creating an ITD. As shown in Figure 6, the sound travels a shorter distance to the right ear than to the left ear; this yields an ITD. For wavelengths roughly equal to, or shorter than, the diameter of the head, a shadowing effect is produced at the ear further from the source, creating an ILD. It is worth mentioning that the ITD for any frequency is theoretically the same for all frequencies for a particular  
Figure 5: NSIM scores derived from the Similarity Map between Reference and Test Gammmatonegrams. Further examples and descriptions can be found in [33]. stimulus location, whereas the Interaural Phase Difference (IPD) will vary according to the frequency of the stimulus [36]. The idea behind the AMBIQUAL algorithm is to use signal information which is embedded in Ambisonic audio channels to calculate QoE as perceived by listeners. The aim is to predict a composite QoE degradation resulting from compression for a given spatial audio signal. It does not consider other factors, e.g., head listening direction with respect to source cues or the influence of the HRTF used in rendering the binaural signal. Unlike existing methods that estimate the direction of audio sources by analysis of binaurally rendered signals [37] or energetic analysis [38; 39] the proposed AMBIQUAL algorithm operates directly on the B-format Ambisonic audio channels to derive Listening Quality and Localization Accuracy metrics. As with ViSQOLAudio, the AMBIQUAL approach uses a time-frequency representation to measure the similarity between a reference and a test audio signal. This time, however, a spectrogram of phase angles rather than magnitudes is used for the signal similarity comparisons. After the pre-processing stage, when the test signal is scaled to match the reference signal's sound pressure level, two \"phaseogram\" representations of the reference and test signals are created. Phaseograms are computed using a 2048-point STFT (with 1536-point Hamming window, 50% overlap). In order to match gammatone frequency bands derived from Equivalent Rectangular Bandwidth scale (i.e., between the lowest central frequency of 50 Hz and highest central frequency of 14,064 Hz) the number of frequency bins is reduced to first 640 (i.e., from 23 Hz to 15,000 Hz). Like before, the reference phaseogram is segmented into patches 480 ms long (30 frames, each 16 ms) and each reference patch is aligned with the corresponding test patch. The similarity between reference and test phaseogram patches are computed frame by frame taking into account all 640 frequency bins. Resulting NSIM scores are averaged across 32 frequency bands corresponding to 32 critical gammatonegram frequency bands. Then, the similarity scores are calculated for each patch separately and averaged over the patches to yield the resulting similarity NSIM score for the test signal.