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Personalized Timbre Optimization for Stereophonic Sound Reproduction via Earphones: Part 2 – Practical Implementation and Validation on Consumer TWS Devices

Authors: Hamasaki, Kimio; Hara, Atsushi; Hirai, Haruto; Tojo, Nao; Saito, Shun; Hosoo, Mitsuru

Express Paper · Paper 438 · May 2026

Abstract

This paper presents Part 2 of our study on personalized timbre optimization for stereophonic sound reproduction via earphones, following our previous work [1]. Part 1 established an auditory-model-based framework, hereafter referred to as PTTC-1 (Personalized Timbre Target response Curve), for reproducing the listener's individual reference for natural timbre. However, its practical application was constrained by the need for high-resolution 3D anatomical scanning, individualized HRTF computation by the boundary element method, and controlled listening tests conducted by trained audio engineers. The present paper introduces PTTC-2, a simplified and fully automated implementation for consumer True Wireless Stereo (TWS) earphones, in which a single smartphone image captures the listener's head and outer ear, the directionally averaged HRTFs are estimated by machine learning, and a brief in-app listening test determines a listener-specific weighting coefficient .
Two empirical studies are reported. First, the analysis of values selected by 2,850 users, together with a follow-up assessment of 29 listeners, indicates that is a listener-specific parameter that is not strongly biased by program material. Second, a controlled subjective experiment with 24 listeners compared PTTC-1, PTTC-2, and two non-individualized target response curves used in consumer products. PTTC-1 and PTTC-2 did not differ significantly in holistic timbre attributes, and both were preferred over non-individualized target response curves. Principal component analysis further revealed that listener preference is governed by an overall naturalness axis rather than by a high-frequency emphasis axis. These results demonstrate that auditory-model-based timbre personalization can be effectively translated into a practical, consumer-ready technology that preserves the perceptual benefits of the full workflow.

Details

AES Convention
160
Paper number
438
Publication date
May 28, 2026
Session subject
AI and Machine Learning in Audio, Audio Applications and Technologies, Audio Equipment, Audio Processing, Perception
Affiliation
final Inc., ARTSRIDGE LLC; final Inc.; final Inc.; final Inc.; final Inc.; final Inc. (See document for exact affiliation information.)
Type
Express Paper