Abstract: A microphone array for capturing sound field audio content may include a first set of directional microphones disposed on a first framework at a first radius from a center and arranged in at least a first portion of a first spherical surface. The microphone array may include a second set of directional microphones disposed on a second framework at a second radius from the center and arranged in at least a second portion of a second spherical surface. The second radius may be larger than the first radius. The directional microphones may capture information that allows for the extraction of Higher-Order Ambisonics (HOA) signals.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Systems and methods for HRTF personalization are provided. More specifically, the systems and methods provide HRTF personalization utilizing non-parametric processing of three-dimensional head scans. Accordingly, the systems and methods for HRTF personalization generate a personalized set of HRTFs for a user without having to extract specific geometric and/or anthropometric features from a three dimensional head scan of a user and/or from the three dimensional head scans of training subjects in a database.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Systems and methods for HRTF personalization are provided. More specifically, the systems and methods provide HRTF personalization utilizing non-parametric processing of three-dimensional head scans. Accordingly, the systems and methods for HRTF personalization generate a personalized set of HRTFs for a user without having to extract specific geometric and/or anthropometric features from a three dimensional head scan of a user and/or from the three dimensional head scans of training subjects in a database.

Abstract: Spherical microphone arrays capture a three-dimensional sound field (P(?c, t)) for generating an Ambisonics representation (Anm(t)), where the pressure distribution on the surface of the sphere is sampled by the capsules of the array. The impact of the microphones on the captured sound field is removed using the inverse microphone transfer function. The equalization of the transfer function of the microphone array is a big problem because the reciprocal of the transfer function causes high gains for small values in the transfer function and these small values are affected by transducer noise. The invention minimizes that noise by using a Wiener filter processing in the frequency domain, which processing is automatically controlled per wave number by the signal-to-noise ratio of the microphone array.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

Abstract: Spherical microphone arrays capture a three-dimensional sound field (P(?c, t)) for generating an Ambisonics representation (Anm(t)), where the pressure distribution on the surface of the sphere is sampled by the capsules of the array. The impact of the microphones on the captured sound field is removed using the inverse microphone transfer function. The equalisation of the transfer function of the microphone array is a big problem because the reciprocal of the transfer function causes high gains for small values in the transfer function and these small values are affected by transducer noise. The invention minimises that noise by using a Wiener filter processing in the frequency domain, which processing is automatically controlled per wave number by the signal-to-noise ratio of the microphone array.

Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.