A head-related transfer function (HRTF) is a mathematical model that describes the acoustic path between a sound source and a listener’s ear. Using binaural synthesis techniques, HRTFs play a crucial role in creating immersive audio experiences through headphones or loudspeakers, using binaural synthesis techniques. HRTF measurements can be conducted either with standardised mannequins or with in-ear microphones on real subjects. However, various challenges arise in, for example, individual differences in head shape, pinnae geometry, and torso dimensions, as well as in the extensive number of measurements required for optimal audio immersion. To address these issues, numerous methods have been developed to generate new HRTFs from existing data or through computer simulations. This review paper provides an overview of the current approaches and technologies for generating, adapting, and optimising HRTFs, with a focus on physical modelling, anthropometric techniques, machine learning methods, interpolation strategies, and their practical applications.

Crossover networks for multi-way loudspeaker systems and audio processing are reviewed, including both analog and digital designs.Ahigh-quality crossover network must maintain a flat overall magnitude response, within small tolerances, and a sufficiently linear phase response. Simultaneously, the crossover filters for each band must provide a steep transition to properly separate the bands, also accounting for the frequency ranges of the drivers. Furthermore, crossover filters affect the polar...

Digital audio equalization is a very common procedure in the acoustic field that allows to improve the listening experience by adjusting the auditory frequency response. The design of graphic equalizers introduces several problems related to the necessity of implementing high performing filters with linear phase, usually characterized by high computational complexity. This paper proposes an innovative linear-phase graphic equalizer based on interpolated finite impulse response (IFIR) filters. IFIR filters seem to be suitable for a graphic equalizer thanks to their properties. In fact, they can reach very narrow transition bands, however with low computational complexity and linear phase, avoiding ripple between adjacent bands. The proposed IFIR equalizer has been compared with some state-of-the-art methods in terms of frequency response, distortion and computational cost. The experimental results have proved the effectiveness of the proposed equalizer, that has shown a considerable reduction on the computational complexity, meanwhile preserving the performances in terms of audio quality.

Head Related Transfer Functions (HRTFs) are a mathematical representation of the acoustic transfer functions between a sound source and the listener's ears. They play an important role for both creating immersive audio scenarios and evaluating the perception of a sound system from human being point of view. These applications are also important for automotive scenario, since the car is nowadays the most used audio listening environment. Automotive audio has been attracting a great deal of attention in recent years as well as the use of HRTFs inside the car for creating these immersive scenarios. In this context, we proposed a technique for HRTFs measurement based on perfect or orthogonal periodic sequences that allows an effective measurement robust towards system nonlinearities. Experiments with an emulated scenario and measurements performed in real environments illustrate the validity of the approach in comparison with other competing HRTFs measurement methods of the state of the art.

This paper presents a new active noise control (ANC) technique with online secondary path estimation and subband adaptive filtering (SAF) for snoring reduction. In particular, the combination with the SAF structure allows to obtain a better estimation of the primary path and also has an impact on the algorithm performance, especially in terms of convergence rate. Although the SAF technique is applied only in the primary path estimation, it improves also the performance of the secondary path estimation guaranteeing a better convergence rate. The proposed algorithm has been first validated using white noise and then tested considering snoring signals. The experimental results have confirmed its effectiveness in comparison with the state of the art.