# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 3 Measurement and Estimation
By analysing how sound changes with head movement in various environments, the method estimatespersonalised HRTFs. The results show that the estimated HRTFs closely match those measured in an anechoic chamber and significantly enhance sound localisation and reduce front-back confusion in virtual environments. Despite its effectiveness, there are limitations, including the need for stationary noise sources during recording, potential challenges with multiple or moving sound sources, and the impact of earbud placement on HRTF accuracy. In [85], a method called UNIQ is presented for estimating a user's personal HRTF by leveraging the movement of a smartphone around the head, combined with mobile acoustic communication between the phone and earphones. Unlike traditional approaches that require controlled environments and precise equipment, UNIQ aims to provide a practical solution for estimating HRTFs at home, making it more accessible for applications such as immersive augmented reality (AR) and virtual reality (VR), earables, and smart hearing aids. The proposed system works by asking users to wear earphones and move a smartphone around their head while it emits pre-designed sounds. These sounds are recorded by the earphones, while the smartphone logs its inertial measurements during the motion. The algorithm then uses these inputs to estimate the user's personal HRTF. This method addresses two main challenges: first, accurately tracking the smartphone's location while accounting for head-related signal diffraction and multipath effects; and second, synthesising a far-field HRTF from near-field measurements, which differ due to variations in signal arrival angles. UNIQ tackles these challenges by modelling the 3D head geometry, applying diffraction effects, and deriving expected signal equations at the ears. It then compares these expectations against acoustic and IMU measurements to optimise the head parameters and phone locations. The near-field HRTF is generated and subsequently used to construct a far-field HRTF using physics-based modelling techniques. As an alternative to direct measurement, estimation techniques rely on user feedback in response to specific auditory stimuli, allowing models to learn and adapt the HRTF based on subjective auditory perception. These techniques have become increasingly powerful tools in the field of HRTF personalisation. Researchers are leveraging various methods to predict and generate HRTFs without the need for direct physical measurements or detailed personal data. By relying on generalizable features or even user preferences, these techniques enable efficient and user-friendly HRTF customisation. Among these, it is particularly worth mentioning [PERSON] et al. (2017) [86], who proposed a method for personalising HRTFs using an adaptive variational autoencoder. The autoencoder is initially trained on a public HRTF dataset to learn general and individual-specific features. During calibration, users listen to pairs of audio signals and indicate their preferences. This feedback is used to tailor the HRTFs to the user's specific preferences without requiring detailed physical measurements. Despite the effectiveness and relatively fast optimisation of this method, it still requires about 30 min for calibration, and assessing the absolute quality of the final personalised HRTFs can be challenging. [PERSON] et al. (2021) [87] present a temporal convolutional network (TCN) that implicity models HRTFs. This method avoids the need for explicit measurements of the listener's physical features, instead learning the HRTF characteristics through training on perceptual data. The model's ability to generate high-quality HRTFs is evaluated through perceptual tests, showing promising results in terms of user satisfaction and sound localisation accuracy. As a complement to HRTF measurements, some approaches require the acquisition of individual-specific data to further enrich HRTF datasets.