LTT Labs has developed a method to estimate an individual's Head-Related Transfer Function (HRTF)—the spectral "fingerprint" of how anatomy shapes sound—using a combination of a Bluetooth speaker, a small in-ear microphone, and a neural network. This approach aims to provide a low-cost alternative to the traditional use of expensive anechoic chambers and high-end measurement rigs, which can cost hundreds of thousands of dollars.
LTT Labs uses AI and Bluetooth speakers to estimate HRTF, offering a low-cost alternative to anechoic chambers
To achieve this, the system uses a mobile setup where the subject is measured in an outdoor environment to minimize reflections. The user provides only 10 directional measurements using a Bluetooth speaker. A neural network, trained on over 1 million directional HRTF observations from 845 individuals, then predicts the remaining 430 directions required for a complete set. In validation tests on subjects not included in the training data, the model achieved an average error of approximately 0.93 dB, which is near the perceptual threshold of 1–2 dB where humans begin to notice frequency response differences.
While promising, the method has limitations. The current training data is demographically narrow, and the model cannot reliably predict frequency responses above 10 kHz or capture precise phase information due to the limitations of Bluetooth synchronization. However, for estimating Diffuse-Field HRTF (DF-HRTF)—the aggregate response of an ear to sound from all directions—the method proves effective. LTT Labs suggests this technology could significantly reduce the cost and time required to collect human reference data, potentially aiding the customization of audio products and hearing aids.
Sources
- 約3000万円もかかる無響室を小型マイクとBluetoothスピーカーで代替 （GIGAZINE, 2026-10-01）
- LTT Labs