This paper considers a functional modeling of a head-related transfer function (HRTF) where the spatial-portion is constrained to be expanded using spherical harmonics and the frequency-portion is expanded in terms of standard closed-form orthonormal functions, which may be user selected. We derive an objective evaluation metric to compare the relative energy efficiencies of candidate functional models using empirical HRTF database measurements and robust estimation techniques. Among four sets of closed-form orthonormal functions the complex exponentials are identified as the most efficient to represent the frequency-portion in the spherical harmonics-based HRTF functional model. The proposed model is evaluated across three HRTF data sets: 1) CIPIC database, 2) the MIT KEMAR (Knowles Electronics Mannequin for Acoustics Research) database, and 3) the ANU KEMAR database.

Existing algorithms for improving speech intelligibility in a noisy environment generally focus on modifying the acoustic features of live, recorded or synthesized speech while preserving the phonetic composition (the message). In this paper, we present an algorithm for text-to-speech systems that operates at a higher level of abstraction, the message-level. We use a paraphrasing system to adjust the linguistic content of the intended message such that the speech intelligibility improves under noisy conditions. To distinguish the intelligibility among paraphrases, we use the numerical integration of a normalized log-likelihood function over different signal-to-noise conditions. Objective evaluation results show that the developed measure is able to distinguish the intelligibility among paraphrases. Results from subjective evaluation confirm the effectiveness of our objective measure.

This paper deals with two important questions associated with HRTF measurement: 1) “what is the required angular resolution?,” and 2) “what is the most suitable sampling scheme?.” The paper shows that a well-defined finite number of spherical harmonics can capture the head-related transfer function (HRTF) spatial variations in sufficient detail, which is defined as the HRTF spatial dimensionality. For the 20-kHz audible frequency range, the value of the dimensionality means a high-directional resolution HRTF measurement is required. Considering such a high-resolution measurement, a number of sampling criteria have been identified from both mechanical setup and data processing aspects. Different sampling candidates are then compared to demonstrate that the best method which satisfies all requirements is the class termed as IGLOO. A fast spherical harmonic transform algorithm based on the IGLOO scheme is developed to accelerate the high-resolution data analysis. The proposed method is validated through simulation and experimental data acquired from a KEMAR mannequin.

This paper consider the problem of how to evaluate the efficiency of a 3D continuous functional HRTF model in rep resenting measured data. The proposed method is based on Karhunen-Loeve theorem. After investigating the optimization problem of representing the process with an finite linear combination of orthogonal functions in L space by means of the least squared method, the variance of random variables involved in the model is found the key metric in efficiency evaluation of a given functional model. Then, the coefficients of the 3D continuous HRTF model are analyzed. The results show that the efficiency of the model in spatial component expansion is around 70% and the best choice in frequency component expansion is the spherical Bessel function.