Dolby Laboratories Patente

A user prompt for a diffusion model to generate an image is received. Noisy latent features generated by denoising layers of the diffusion model are collected based on the user prompt and are to generate a pre-customized version of the image. Pre-customized lighting properties of the pre-customized version of the image are predicted based on the noisy latent features. Light source properties of a light source to be logically applied to generate a customized version of the image are determined. The light source properties of the light source are applied to the pre-customized lighting properties of the pre-customized version of the image to generate customized lighting properties of the customized version of the image. A loss computed from the customized lighting properties and the pre-customized lighting properties is used to guide the diffusion model to generate the customized version of the image.

The positions of a plurality of speakers at a media consumption site are determined. Audio information in an object-based format is received. Gain adjustment value for a sound content portion in the object-based format may be determined based on the position of the sound content portion and the positions of the plurality of speakers. Audio information in a ring-based channel format is received. Gain adjustment value for each ring-based channel in a set of ring-based channels may be determined based on the ring to which the ring-based channel belongs and the positions of the speakers at a media consumption site.

This disclosure falls into the field of audio coding, in particular it is related to the field of providing a framework for providing loudness consistency among differing audio output signals. In particular, the disclosure relates to methods, computer program products and apparatus for encoding and decoding of audio data bitstreams in order to attain a desired loudness level of an output audio signal.

Methods, apparatus, programs, and storage media for enhancing speech signals based on a neural network are provided. The method includes receiving a speech signal and a corresponding reference speech signal. A first set of band features is extracted from the speech signal and a second set of band features is extracted from the reference speech signal, wherein each feature in the first set of band features corresponds to a frequency band of the speech signal and each feature in the second set of band features corresponds to a frequency band of the reference speech signal. The first set of band features is split into a low frequency band part and high frequency band part. Band features corresponding to the high frequency band part are mapped to a reduced number of high frequency band features. The low frequency band part and the reduced number of high frequency band features are combined into a third set of band features. A first mask is determined by inputting the third set of band features into a speech enhancement neural subnetwork. The first mask is split into a low frequency mask part and high frequency mask part. Entries corresponding to the high frequency mask part are mapped to an increased number of entries. The low frequency mask part and the increased number of entries are combined into a second mask. A fourth set of band features is determined based on the second mask and the speech signal. A loss function is evaluated based on the second set of band features and the fourth set of band features. Weights of the speech enhancement neural subnetwork are updated based on a value of the evaluated loss function.

An audio processing unit, comprising a buffer memory (201) configured to store at least one frame of an encoded audio bitstream which includes audio data and a metadata container having a header and one or more metadata payloads after the header, a parser (205) coupled to the buffer memory, and an audio decoder (202) coupled to the parser. The one or more metadata payloads include dynamic range compression (DRC) metadata including DRC control values indicative of a compression profile assumed by an encoder to generate said DRC control values. The audio decoder is configured to perform dynamic range compression on decoded audio data generated by decoding said at least some of the audio data, using the DRC metadata.

The present document describes a method (500) for generating a bitstream (101), wherein the bitstream (101) comprises a sequence of superframes (400) for a sequence of frames of an immersive audio signal (111). The method (500) comprises, repeatedly for the sequence of superframes (400), inserting (501) coded audio data (206) for one or more frames of one or more downmix channel signals (203) derived from the immersive audio signal (111), into data fields (411, 421, 412, 422) of a superframe (400); and inserting (502) metadata (202, 205) for reconstructing one or more frames of the immersive audio signal (111) from the coded audio data (206), into a metadata field (403) of the superframe (400).

Many portable playback devices cannot decode and playback encoded audio content having wide bandwidth and wide dynamic range with consistent loudness and intelligibility unless the encoded audio content has been prepared specially for these devices. This problem can be overcome by including with the encoded content some metadata that specifies a suitable dynamic range compression profile by either absolute values or differential values relative to another known compression profile. A playback device may also adaptively apply gain and limiting to the playback audio. Implementations in encoders, in transcoders and in decoders are disclosed.

Disclosed is an audio signal encoding/decoding method that uses an encoding downmix strategy applied at an encoder that is different than a decoding re-mix/upmix strategy applied at a decoder. Based on the type of downmix coding scheme, the method comprises: computing input downmixing gains to be applied to the input audio signal to construct a primary downmix channel; determining downmix scaling gains to scale the primary downmix channel; generating prediction gains based on the input audio signal, the input downmixing gains and the downmix scaling gains; determining residual channel(s) from the side channels by using the primary downmix channel and the prediction gains to generate side channel predictions and subtracting the side channel predictions from the side channels; determining decorrelation gains based on energy in the residual channels; encoding the primary downmix channel, the residual channel(s), the prediction gains and the decorrelation gains; and sending the bitstream to a decoder.

A quantization parameter signalling mechanism for both SDR and HDR content in video coding is described using two approaches. The first approach is to send the user-defined Qpc table directly in high level syntax. This leads to more flexible and efficient QP control for future codec development and video content coding. The second approach is to signal luma and chroma QPs independently. This approach eliminates the need for Qp_C tables and removes the dependency of chroma quantization parameter on luma QP.

Encoding/decoding an immersive voice and audio services (IVAS) bitstream comprises: encoding/decoding a coding mode indicator in a common header (CH) section of an IVAS bitstream, encoding/decoding a mode header or tool header in the tool header (TH) section of the bitstream, the TH section following the CH section, encoding/decoding a metadata payload in a metadata payload (MDP) section of the bitstream, the MDP section following the CH section, encoding/decoding an enhanced voice services (EVS) payload in an EVS payload (EP) section of the bitstream, the EP section following the CH section, and on the encoder side, storing or streaming the encoded bitstream, and on the decoder side, controlling an audio decoder based on the coding mode, the tool header, the EVS payload, and the metadata payload or storing a representation of same.

Embodiments are disclosed for spatial noise filling in multi-channel codecs. In an embodiment, a method of regenerating background noise ambience in a multi-channel codec by generating spatial hole filling noise comprises: computing noise estimates based on a primary downmix channel generated from an input audio signal representing a spatial audio scene with background noise ambience; computing spectral shaping filter coefficients based on the noise estimates; spectrally shaping the multi-channel noise signal using the spectral shaping filter coefficients and a noise distribution, the spectral shaping resulting in a diffused, multi-channel noise signal with uncorrelated channels; spatially shaping the diffused, uncorrelated multi-channel noise signal with uncorrelated channels based on a noise ambience of the spatial audio scene; and adding the spatially and spectrally shaped multi-channel noise to a multi-channel codec output to synthesize the background noise ambience of the spatial audio scene.

The present disclosure relates to a method and system for predicting a future orientation of an orientation tracker (100). The method comprising obtaining a sequence of angular velocity samples, each angular velocity sample indicating an angular velocity at a point in time and obtaining a sequence of angular acceleration samples, each angular acceleration sample indicating an acceleration or deceleration of the angular velocity at each point in time. Wherein said method further comprises determining (S5a), for each point in time where the angular velocity is accelerating, a predicted orientation of the orientation tracker (100) based on a first order prediction of an accumulated rotation of the orientation tracker (100) and determining (S5c), for each point in time where the angular velocity is decelerating, a predicted orientation of the orientation tracker (100) based on a second order prediction of the accumulated rotation of the orientation tracker (100).

Smaller halftone tiles are implemented on a first modulator of a dual modulation projection system. This techniques uses multiple halftones per frame in the pre-modulator synchronized with a modified bit sequence in the primary modulator to effectively increase the number of levels provided by a given tile size in the halftone modulator. It addresses the issue of reduced contrast ratio at low light levels for small tile sizes and allows the use of smaller PSFs which reduce halo artifacts in the projected image and may be utilized in 3D projecting and viewing.

Described herein is a method of encoding Higher Order Ambisonics, HOA, audio, the method including: receiving an input HOA audio signal having more than four Ambisonics channels; encoding the HOA audio signal using a SPAR coding framework and a core audio encoder; and providing the encoded HOA audio signal to a downstream device, the encoded HOA audio signal including core encoded SPAR downmix channels and encoded SPAR metadata. Further described are a method of decoding Higher Order Ambisonics, HOA, audio, respective apparatuses and computer program products.

A system for generating enhanced speech data using robust audio features is disclosed. In some embodiments, a system is programmed to use a self-supervised deep learning model to generate a set of feature vectors from given audio data that contains contaminated speech and is coded. The system is further programmed to use a generative deep learning model to create improved audio data corresponding to clean speech from the set of feature vectors.

Methods for generating an object based audio program, renderable in a personalizable manner, and including a bed of speaker channels renderable in the absence of selection of other program content (e.g., to provide a default full range audio experience). Other embodiments include steps of delivering, decoding, and/or rendering such a program. Rendering of content of the bed, or of a selected mix of other content of the program, may provide an immersive experience. The program may include multiple object channels (e.g., object channels indicative of user-selectable and user-configurable objects), the bed of speaker channels, and other speaker channels. Another aspect is an audio processing unit (e.g., encoder or decoder) configured to perform, or which includes a buffer memory which stores at least one frame (or other segment) of an object based audio program (or bitstream thereof) generated in accordance with, any embodiment of the method.

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