Abstract: A method for estimating a user's location in an environment may involve receiving output signals from each microphone of a plurality of microphones in the environment. At least two microphones of the plurality of microphones may be included in separate devices at separate locations in the environment and the output signals may correspond to a current utterance of a user. The method may involve determining multiple current acoustic features from the output signals of each microphone and applying a classifier to the multiple current acoustic features. Applying the classifier may involve applying a model trained on previously-determined acoustic features derived from a plurality of previous utterances made by the user in a plurality of user zones in the environment. The method may involve determining, based at least in part on output from the classifier, an estimate of the user zone in which the user is currently located.

Abstract: Some disclosed methods involve multi-band bass management. Some such examples may involve applying multiple high-pass and low-pass filter frequencies for the purpose of bass input management. Some disclosed methods treat at least some low-frequency signals as audio objects that can be panned. Some disclosed methods involve panning low and high frequencies separately. Following high-pass rendering, a power audit may determine a low-frequency deficit factor that is to be reproduced by subwoofers or other low-frequency-capable loudspeakers.

Abstract: Some disclosed methods involve encoding or decoding directional audio data. Some encoding methods may involve receiving a mono audio signal corresponding to an audio object and a representation of a radiation pattern corresponding to the audio object. The radiation pattern may include sound levels corresponding to plurality of sample times, a plurality of frequency bands and a plurality of directions. The methods may involve encoding the mono audio signal and encoding the source radiation pattern to determine radiation pattern metadata. Encoding the radiation pattern may involve determining a spherical harmonic transform of the representation of the radiation pattern and compressing the spherical harmonic transform to obtain encoded radiation pattern metadata.

Abstract: A multi-stream rendering system and method may render and play simultaneously a plurality of audio program streams over a plurality of arbitrarily placed loudspeakers. At least one of the program streams may be a spatial mix. The rendering of said spatial mix may be dynamically modified as a function of the simultaneous rendering of one or more additional program streams. The rendering of one or more additional program streams may be dynamically modified as a function of the simultaneous rendering of the spatial mix.

Abstract: A method may involve receiving output signals from each microphone of a plurality of microphones in the environment, each of the plurality of microphones residing in a microphone location of the environment, the output signals corresponding to an utterance of a person. The method may involve determining, based at least in part on the output signals, a zone within the environment that has at least a threshold probability of including the person's location and generating a plurality of spatially-varying attentiveness signals within the zone. Each attentiveness signal may be generated by a device located within the zone. Each attentiveness signal may indicate that a corresponding device is in an operating mode in which the corresponding device is awaiting a command and may indicate a relevance metric of the corresponding device.

Abstract: The present document describes a method (400) for encoding a soundfield representation (SR) input signal (101, 301) describing a soundfield at a reference position, wherein the SR input signal (101, 301) comprises a plurality of channels for a plurality of different directivity patterns of the soundfield at the reference position. The method (400) comprises extracting (401) one or more audio objects (103, 303) from the SR input signal (101, 301). Furthermore, the method (400) comprises determining (402) a residual signal (102, 302) based on the SR input signal (101, 301) and based on the one or more audio objects (103, 303). The method (400) also comprises performing joint coding of the one or more audio objects (103, 303) and/or the residual signal (102, 302). In addition, the method (400) comprises generating (403) a bitstream (701) based on data generated in the context of joint coding of the one or more audio objects (103, 303) and/or the residual signal (102, 302).

Abstract: Some disclosed methods involve encoding or decoding directional audio data. Some encoding methods may involve receiving a mono audio signal corresponding to an audio object and a representation of a radiation pattern corresponding to the audio object. The radiation pattern may include sound levels corresponding to plurality of sample times, a plurality of frequency bands and a plurality of directions. The methods may involve encoding the mono audio signal and encoding the source radiation pattern to determine radiation pattern metadata. Encoding the radiation pattern may involve determining a spherical harmonic transform of the representation of the radiation pattern and compressing the spherical harmonic transform to obtain encoded radiation pattern metadata.

Abstract: Some disclosed methods involve multi-band bass management. Some such examples may involve applying multiple high-pass and low-pass filter frequencies for the purpose of bass input management. Some disclosed methods treat at least some low-frequency signals as audio objects that can be panned. Some disclosed methods involve panning low and high frequencies separately. Following high-pass rendering, a power audit may determine a low-frequency deficit factor that is to be reproduced by subwoofers or other low-frequency-capable loudspeakers.

Abstract: Some disclosed methods involve encoding or decoding directional audio data. Some encoding methods may involve receiving a mono audio signal corresponding to an audio object and a representation of a radiation pattern corresponding to the audio object. The radiation pattern may include sound levels corresponding to plurality of sample times, a plurality of frequency bands and a plurality of directions. The methods may involve encoding the mono audio signal and encoding the source radiation pattern to determine radiation pattern metadata. Encoding the radiation pattern may involve determining a spherical harmonic transform of the representation of the radiation pattern and compressing the spherical harmonic transform to obtain encoded radiation pattern metadata.

Abstract: The present document describes a method (400) for encoding a soundfield representation (SR) input signal (101, 301) describing a soundfield at a reference position, wherein the SR input signal (101, 301) comprises a plurality of channels for a plurality of different directivity patterns of the soundfield at the reference position. The method (400) comprises extracting (401) one or more audio objects (103, 303) from the SR input signal (101, 301). Furthermore, the method (400) comprises determining (402) a residual signal (102, 302) based on the SR input signal (101, 301) and based on the one or more audio objects (103, 303). The method (400) also comprises performing joint coding of the one or more audio objects (103, 303) and/or the residual signal (102, 302). In addition, the method (400) comprises generating (403) a bitstream (701) based on data generated in the context of joint coding of the one or more audio objects (103, 303) and/or the residual signal (102, 302).

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: 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.