Abstract: Encoding/decoding an audio signal having one or more audio components, wherein each audio component is associated with a spatial location. A first audio signal presentation (z) of the audio components, a first set of transform parameters (w(f)), and signal level data (?2) are encoded and transmitted to the decoder. The decoder uses the first set of transform parameters (w(f)) to form a reconstructed simulation input signal intended for an acoustic environment simulation, and applies a signal level modification (?) to the reconstructed simulation input signal. The signal level modification is based on the signal level data (?2) and data (p2) related to the acoustic environment simulation. The attenuated reconstructed simulation input signal is then processed in an acoustic environment simulator. With this process, the decoder does not need to determine the signal level of the simulation input signal, thereby reducing processing load.

Abstract: A method of audio content identification includes using a two-stage classifier. The first stage includes previously-existing classifiers and the second stage includes a new classifier. The outputs of the first and second stages calculated over different time periods are combined to generate a steering signal. The final classification results from a combination of the steering signal and the outputs of the first and second stages. In this manner, a new classifier may be added without disrupting existing classifiers.

Abstract: Disclosed is a method for managing acoustic feedback in real-time audio communications in a communications system, the method comprising determining, by means of a detection module, whether a first communication device is in loudspeaker mode, whether the first communication device is in real-time audio communications with a second communication, and whether the first communication device and the second communication device are in a same acoustic space. Upon determining that this is the case a request signal for requesting one or more measures against acoustic feedback is provided to a mitigation module. Further disclosed are a device and a system configured to perform the method, a non-transitory computer-readable medium, an encoder and a decoder.

Abstract: Methods and systems for frame rate scalability are described. Support is provided for input and output video sequences with variable frame rate and variable shutter angle across scenes, or for input video sequences with fixed input frame rate and input shutter angle, but allowing a decoder to generate a video output at a different output frame rate and shutter angle than the corresponding input values. Techniques allowing a decoder to decode more computationally-efficiently a specific backward compatible target frame rate and shutter angle among those allowed are also presented.

Abstract: 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.

Abstract: A method (900) for rendering audio in a virtual reality rendering environment (180) is described. The method (900) comprises rendering (901) an origin audio signal of an origin audio source (113) of an origin audio scene (111) from an origin source position on a sphere (114) around a listening position (201) of a listener (181). Furthermore, the method (900) comprises determining (902) that the listener (181) moves from the listening position (201) within the origin audio scene (111) to a listening position (202) within a different destination audio scene (112). In addition, the method (900) comprises applying (903) a fade-out gain to the origin audio signal to determine a modified origin audio signal, and rendering (903) the modified origin audio signal of the origin audio source (113) from the origin source position on the sphere (114) around the listening position (201, 202).

Abstract: In one embodiment, methods, media, and systems process and display light field images using a view function that is based on pixel locations in the image and on the viewer's distance (observer's Z position) from the display. The view function can be an angular view function that specifies different angular views for different pixels in the light field image based on the inputs that can include: the x or y pixel location in the image, the viewer's distance from the display, and the viewer's angle relative to the display. In one embodiment, light field metadata, such as angular range metadata and/or angular offset metadata can be used to process and display the image. In one embodiment, color volume mapping metadata can be used to adjust color volume mapping based on the determined angular views; and the color volume mapping metadata can also be adjusted based on angular offset metadata.

Abstract: A method (600) for decoding an encoded audio signal (102) is described. The encoded audio signal (102) comprises a sequence of frames. Furthermore, the encoded audio signal (102) is indicative of a plurality of different dynamic range control (DRC) profiles for a corresponding plurality of different rendering modes. Different subsets of DRC profiles from the plurality of DRC profiles are comprised within different frames of the sequence of frames, such that two or more frames of the sequence of frames jointly comprise the plurality of DRC profiles.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

Abstract: In an embodiment, a method comprises: filtering reference audio content items to separate the reference audio content items into different frequency bands; for each frequency band, extracting a first feature vector from at least a portion of each of the reference audio content items, wherein the first feature vector includes at least one audio characteristic of the reference audio content items; obtaining at least one semantic label from at least a portion of each of the reference audio content items; obtaining a second feature vector consisting of the first feature vectors per frequency band and the at least one semantic label; generating, based on the second feature vector, cluster feature vectors representing centroids of clusters; separating the reference audio content items according to the cluster feature vectors; and computing an average target profile for each cluster based on the reference audio content items in the cluster.

Abstract: A method may involve: receiving direction of arrival (DOA) data corresponding to sound emitted by at least a first smart audio device of the audio environment that includes a first audio transmitter and a first audio receiver, the DOA data corresponding to sound received by at least a second smart audio device of the audio environment that includes a second audio transmitter and a second audio receiver, the DOA data corresponding to sound emitted by at least the second smart audio device and received by at least the first smart audio device; receiving one or more configuration parameters corresponding to the audio environment, to one or more audio devices, or both; and minimizing a cost function based at least in part on the DOA data and the configuration parameter(s), to estimate a position and an orientation of at least the first smart audio device and the second smart audio device.

Abstract: Described herein is a method for training a machine learning algorithm. The method may comprise receiving a first input multichannel audio signal. The method may comprise generating, using the machine learning algorithm, an intermediate audio signal based on the first input multichannel audio signal. The method may comprise rendering the intermediate audio signal into a first output multichannel audio signal. Further, the method may comprise improving the machine learning algorithm based on a difference between the first input multichannel audio signal and the first output multichannel audio signal. Described herein are further an apparatus for generating an intermediate audio format from an input multichannel audio signal as well as a respective computer program product comprising a computer-readable storage medium with instructions adapted to carry out said method when executed by a device having processing capability.

Abstract: On the basis of a bitstream (P), an n-channel audio signal (X) is reconstructed by deriving an m-channel core signal (Y) and multichannel coding parameters (a) from the bitstream, where 1?m<n. Also derived from the bitstream are pre-processing dynamic range control, DRC, parameters (DRC2) quantifying an encoder-side dynamic range limiting of the core signal. The n-channel audio signal is obtained by parametric synthesis in accordance with the multichannel coding parameters and while cancelling any encoder-side dynamic range limiting based on the pre-processing DRC parameters. In particular embodiments, the reconstruction further includes use of compensated post-processing DRC parameters quantifying a potential decoder-side dynamic range compression. Cancellation of an encoder-side range limitation and range compression are preferably performed by different decoder-side components. Cancellation and compression may be coordinated by a DRC pre-processor.

Abstract: Described herein is a method of processing an audio signal using a deep-learning-based generator, wherein the method includes the steps of: (a) inputting the audio signal into the generator for processing the audio signal; (b) mapping a time segment of the audio signal to a latent feature space representation, using an encoder stage of the generator; (c) upsampling the latent feature space representation using a decoder stage of the generator, wherein at least one layer of the decoder stage applies sinusoidal activation; and (d) obtaining, as an output from the decoder stage of the generator, a processed audio signal. Described are further a method for training said generator and respective apparatus, systems and computer program products.

Abstract: Disclosed are methods and systems which convert a multi-microphone input signal to a multichannel output signal making use of a time- and frequency-varying matrix. For each time and frequency tile, the matrix is derived as a function of a dominant direction of arrival and a steering strength parameter. Likewise, the dominant direction and steering strength parameter are derived from characteristics of the multi-microphone signals, where those characteristics include values representative of the inter-channel amplitude and group-delay differences.

Abstract: Methods and systems for frame rate scalability are described. Support is provided for input and output video sequences with variable frame rate and variable shutter angle across scenes, or for input video sequences with fixed input frame rate and input shutter angle, but allowing a decoder to generate a video output at a different output frame rate and shutter angle than the corresponding input values. Techniques allowing a decoder to decode more computationally-efficiently a specific backward compatible target frame rate and shutter angle among those allowed are also presented.

Abstract: Method for encoding scene-based audio is provided. In some implementations, the method involves determining, by an encoder, a spatial direction of a dominant sound component in a frame of an input audio signal. In some implementations, the method involves determining rotation parameters based on the determined spatial direction and a direction preference of a coding scheme to be used to encode the input audio signal. In some implementations, the method involves rotating sound components of the frame based on the rotation parameters such that, after being rotated, the dominant sound component has a spatial direction that aligns with the direction preference of the coding scheme. In some implementations, the method involves encoding the rotated sound components of the frame of the input audio signal using the coding scheme in connection with an indication of the rotation parameters or an indication of the spatial direction of the dominant sound component.

Abstract: The present disclosure provides a decoder configured to receive a finite bitrate stream that includes a quantized latent frame, where the quantized latent frame includes a quantized representation of a current frame of a signal in a latent domain different from a first domain; to generate a reconstructed latent frame from the quantized latent frame; to use a generative neural network model to perform a task for which the general neural network model has been trained, wherein the task includes to generate parameters for an invertible mapping from the latent domain to the first domain; to reconstruct a current frame of the signal in the first domain, which includes to map the reconstructed latent frame to the first domain by use of the invertible mapping, and to use the reconstructed current frame of the signal in the first domain to update a state of the generative neural network model.

Abstract: In some embodiments, a method, comprises: dividing, using at least one processor, an audio input into speech and non-speech segments; for each frame in each non-speech segment, estimating, using the at least one processor, a time-varying noise spectrum of the non-speech segment; for each frame in each speech segment, estimating, using the at least one processor, speech spectrum of the speech segment; for each frame in each speech segment, identifying one or more non-speech frequency components in the speech spectrum; comparing the one or more non-speech frequency components with one or more corresponding frequency components in a plurality of estimated noise spectra and selecting the estimated noise spectrum from the plurality of estimated noise spectra based on a result of the comparing.

Abstract: Described is a method of training a deep-learning-based system for sound source separation. The system comprises a separation stage for frame-wise extraction of representations of sound sources from a representation of an audio signal, and a clustering stage for generating, for each frame, a vector indicative of an assignment permutation of extracted frames of representations of sound sources to respective sound sources. The representation of the audio signal is a waveform-based representation. The separation stage is trained using frame-level permutation invariant training. Further, the clustering stage is trained to generate embedding vectors for the frames of the audio signal that allow to determine estimates of respective assignment permutations between extracted sound signals and labels of sound sources that had been used for the frames. Also described is a method of using the deep-learning-based system for sound source separation.

Abstract: A method for compressing a HOA signal being an input HOA representation with input time frames (C(k)) of HOA coefficient sequences comprises spatial HOA encoding of the input time frames and subsequent perceptual encoding and source encoding. Each input time frame is decomposed (802) into a frame of predominant sound signals (XPS(k?1)) and a frame of an ambient HOA component ({tilde over (C)}AMB(k?1)). The ambient HOA component ({tilde over (C)}AMB(k?1)) comprises, in a layered mode, first HOA coefficient sequences of the input HOA representation (cn(k?1)) in lower positions and second HOA coefficient sequences (cAMB,n(k?1)) in remaining higher positions. The second HOA coefficient sequences are part of an HOA representation of a residual between the input HOA representation and the HOA representation of the predominant sound signals.

Abstract: The present document describes a method (700) for encoding a multi-channel input signal (201). The method (700) comprises determining (701) a plurality of downmix channel signals (203) from the multi-channel input signal (201) and performing (702) energy compaction of the plurality of downmix channel signals (203) to provide a plurality of compacted channel signals (404). Furthermore, the method (700) comprises determining (703) joint coding metadata (205) based on the plurality of compacted channel signals (404) and based on the multi-channel input signal (201), wherein the joint coding metadata (205) is such that it allows upmixing of the plurality of compacted channel signals (404) to an approximation of the multi-channel input signal (201). In addition, the method (700) comprises encoding (704) the plurality of compacted channel signals (404) and the joint coding metadata (205).

Abstract: In a method to improve the coding efficiency of high-dynamic range (HDR) images, a decoder parses sequence processing set (SPS) data from an input coded bitstream to detect that an HDR extension syntax structure is present in the parsed SPS data. It extracts from the HDR extension syntax structure post-processing information that includes one or more of a color space enabled flag, a color enhancement enabled flag, an adaptive_reshaping_enabled_flag, a dynamic range conversion flag, a color correction enabled flag, or an SDR_viewable_flag. It decodes the input bitstream to generate a preliminary output decoded signal, and generates a second output signal based on the preliminary output signal and the post-processing information.

Abstract: An input image of a first bit depth in an input domain is received. Forward reshaping operations are performed on the input image to generate a forward reshaped image of a second bit depth in a reshaping domain. An image container containing image data derived from the forward reshaped image is encoded into an output video signal of the second bit depth.

Abstract: 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.

Abstract: An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

Abstract: A method, an apparatus, and logic to post-process raw gains determined by input processing to generate post-processed gains, comprising using one or both of delta gain smoothing and decision-directed gain smoothing. The delta gain smoothing comprises applying a smoothing filter to the raw gain with a smoothing factor that depends on the gain delta: the absolute value of the difference between the raw gain for the current frame and the post-processed gain for a previous frame. The decision-directed gain smoothing comprises converting the raw gain to a signal-to-noise ratio, applying a smoothing filter with a smoothing factor to the signal-to-noise ratio to calculate a smoothed signal-to-noise ratio, and converting the smoothed signal-to-noise ratio to determine the second smoothed gain, with smoothing factor possibly dependent on the gain delta.

Abstract: Methods and systems of improving bass response for a speaker in a portable computing device are described. One portable computing device includes first and second cover parts that are joined together to form a casing of the portable computing device, wherein a speaker volume is formed between portions of the first and second cover parts; a speaker arranged within the speaker volume; and one or more elastic spacers arranged between the first and second cover parts. The one or more elastic spacers are arranged to counteract, by their elastic recoil forces, a compression of the speaker volume when the first and second cover parts are under external compressing forces. The one or more elastic spacers are arranged between the first and second cover parts to be partially compressed by the first and second cover parts in the absence of external compressing forces on the first and second cover parts.

Abstract: Methods and apparatus for decoding a compressed Higher Order Ambisonics (HOA) representation of a sound or soundfield. The method may include receiving a bit stream containing the compressed HOA representation and decoding, based on a determination that there are multiple layers, the compressed HOA representation from the bitstream to obtain a sequence of decoded HOA representations. A first subset of the sequence of decoded HOA representations is determined based only on corresponding ambient HOA components. A second subset of the sequence of decoded HOA representations is determined based on corresponding ambient HOA components and corresponding predominant sound components.

Abstract: An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

Abstract: Some implementations involve receiving, from a first subband domain acoustic echo canceller (AEC) of a first audio device in an audio environment, first adaptive filter management data from each of a plurality of first adaptive filter management modules, each first adaptive filter management module corresponding to a subband of the first subband domain AEC, each first adaptive filter management module being configured to control a first plurality of adaptive filters. The first plurality of adaptive filters may include at least a first adaptive filter type and a second adaptive filter type. Some implementations involve extracting, from the first adaptive filter management data, a first plurality of extracted features corresponding to a plurality of subbands of the first subband domain AEC and estimating a current local acoustic state based, at least in part, on the first plurality of extracted features.

Abstract: Embodiments are disclosed for timestamp smoothing to remove jitter. In some embodiments, a method of smoothing timestamps associated with audio packets comprises: receiving, using at least one processor, a series of input timestamps for audio packets and their respective packet lengths; estimating, using the at least one processor, an initial timestamp based on the series of input timestamps, the packet lengths and a sample time; calculating, using the at least one processor, a predicted timestamp based on the estimated initial timestamp; and smoothing, using the at least one processor, the predicted timestamp.

Abstract: Methods for generating an object based audio program which is renderable in a personalizable manner, e.g., to provide an immersive, perception of audio content of the program. Other embodiments include steps of delivering (e.g., broadcasting), decoding, and/or rendering such a program. Rendering of audio objects indicated by the program may provide an immersive experience. The audio content of the program may be indicative of multiple object channels (e.g., object channels indicative of user-selectable and user-configurable objects, and typically also a default set of objects which will be rendered in the absence of a selection by a user) and a bed of 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.

Abstract: An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

Abstract: The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values.

Abstract: In an embodiment, a method comprises: receiving bands of power spectra of an input audio signal and a microphone covariance, and for each band: estimating, using a classifier, respective probabilities of speech and noise; estimating, using a directionality model, a set of means for speech and noise, or a set of means and covariances for speech and noise, based on the microphone covariance for the band and the probabilities; estimating, using a level model, a mean and covariance of noise power based on the probabilities and the power spectra; determining a first noise suppression gain based on the directionality model; determining a second noise suppression gain based on the level model; selecting the first or second noise suppression gain or their sum based on a signal-to-noise ratio of the input audio signal; and scaling a time-frequency representation of the input signal by the selected noise suppression gain.

Abstract: A distributed amplification and packetized audio transmission system for clock synchronization and alignment between an audio/power source and endpoints with dedicated amplifiers and speakers. An Ethernet audio signal is combined with a Power-Line Communications (PLC) signal for transmission from the source to the endpoints over a common conductor. A single master clock in the source synchronizes the Ethernet audio transmitter with the PLC transmitter. Each end-point has a PLC receiver to recover the master clock for use by its Ethernet audio receiver to provide reliable clock synchronization between the source clock and the endpoint clocks. The endpoints can adjust and re-timestamp the PTP packetized clock based upon symbol and timing information from the PLC receiver.

Abstract: Systems, methods, and computer program products for audio processing based on convolutional neural network (CNN) are described. A first CNN architecture may comprise a contracting path of a U-net, a multi-scale CNN, and an expansive path of a U-net. The contracting path may comprise a first encoding layer and may be configured to generate an output representation of the contracting path. The multi-scale CNN may be configured to generate, based on the output representation of the contracting path, an intermediate representation. The multi-scale CNN may comprise at least two parallel convolution paths. The expansive path may comprise a first decoding layer and may be configured to generate a final representation based on the intermediate representation generated by the multi-scale CNN.

Abstract: A neural network system is provided, implementing a generative model for autoregressively generating a distribution for a plurality of current filter-bank samples of an audio signal, wherein the current samples correspond to a current time slot, and each current sample corresponds to a channel of the filter-bank. The system includes a hierarchy of a plurality of neural network processing tiers ordered from a top to a bottom tier, each tier trained to generate conditioning information based on previous filter-bank samples and, for at least each tier but the top tier, also on the conditioning information from a tier higher up in the hierarchy, and an output stage trained to generate the probability distribution based on previous samples for one or more previous time slots and the conditioning information from the lowest processing tier.

Abstract: Described herein is a method of processing an audio signal using a neural network or using a first and a second neural network. Described is further a method of training said neural network or of jointly training a set of said first and said second neural network. Moreover, described is a method of obtaining and transmitting a latent feature space representation of a perceptual domain audio signal using a neural network and a method of obtaining an audio signal from a latent feature space representation of a perceptual domain audio signal using a neural network. Described are also respective apparatuses and computer program products.

Abstract: A neural network system for predicting frequency coefficients of a media signal, the neural network system comprising a time predicting portion including at least one neural network trained to predict a first set of output variables representing a specific frequency band of a current time frame given coefficients of one or several previous time frames, and a frequency predicting portion including a at least one neural network trained to predict a second set of output variables representing a specific frequency band given coefficients of one or several frequency bands adjacent to the specific frequency band in said current time frame. Such a neural network system forms a predictor capable of capturing both temporal and frequency dependencies occurring in time-frequency tiles of a media signal.

Abstract: A projection system and calibration method therefore relate to a light source configured to emit a light in response to an image data, an illumination optical system configured to steer the light, the illumination optical system including a fold mirror and an integrating rod, a digital micromirror device (DMD) including a plurality of micromirrors respectively configured to reflect the steered light to a predetermined location as on-state light or to reflect the steered light as off-state light to a light dump; determining a deviation between an actual angle of orientation and an expected angle of orientation of a respective micromirror of the plurality of micromirrors; calculating a first amount of rotational adjustment corresponding to the fold mirror and a second amount of lateral adjustment corresponding to the integrating rod, and actuating the fold minor and integrating rod according to the corresponding first and second amount.

Abstract: Embodiments are described for a high-frequency waveguide that improves the performance of large-scale surround sound and immersive audio environments. A horn waveguide is configured to be asymmetric about one of a vertical axis and horizontal axis of the waveguide to form an asymmetric horn waveguide. A spherical enclosure surrounds the asymmetric horn waveguide to form a horn speaker, and a three-axis mounting system is configured to fix the horn speaker to one of a wall or ceiling surface of the venue, wherein the mounting system facilitates rotating the horn speaker to a location that provides maximum coverage of the venue within the passband of the asymmetric horn waveguide.

Abstract: In a cloud-based system for encoding high dynamic range (HDR) video, each node receives a video segment and bumper frames. Each segment is subdivided into primary scenes and secondary scenes to derive scene-based forward reshaping functions that minimize the amount of reshaping-related metadata when coding the video segment. When a parent scene of a secondary scene is processed by two or more neighboring nodes, initial forward reshaping functions and trim-pass correction parameters are adjusted using reference tone-mapping functions and updated scene-based trim-pass correction parameters.

Abstract: A projection system and calibration method therefor relate to a light source configured to emit a light in response to an image data, an illumination optical system configured to steer the light, the illumination optical system including a first lens group and a second lens group, a digital micromirror device (DMD) including a plurality of micromirrors respectively configured to reflect the steered light to a predetermined location as on-state light or to reflect the steered light as off-state light to a light dump; determining a deviation between an actual angle of orientation and an expected angle of orientation of a respective micromirror of the plurality of micromirrors; calculating a first amount of lateral adjustment corresponding to the first lens group and a second amount of lateral adjustment corresponding to the second lens group, and actuating the first and second lens groups according to the corresponding first and second amount.

Abstract: Embodiments are described for a method of rendering audio for playback through headphones comprising receiving digital audio content, receiving binaural rendering metadata generated by an authoring tool processing the received digital audio content, receiving playback metadata generated by a playback device, and combining the binaural rendering metadata and playback metadata to optimize playback of the digital audio content through the headphones.

Abstract: The present invention relates to a method for predicting transform coefficients representing frequency content of an adaptive block length media signal, by receiving a frame and receiving block length information indicating a number of quantized transform coefficients for each block in the frame, the number of quantized transform coefficients being one of a first or second number, wherein the first number is greater than the second number, determining a first block has the second number of quantized transform coefficients, converting the first block into a converted block having the first number of quantized transform coefficients, conditioning a main neural network trained to predict at least one output variable given at least one conditioning variable, the at least one conditioning variable being based on information regarding the converted block and block length information for the first block, providing at least one predicted transform coefficients from an output stage of the main neural network.

Abstract: Improved tools for authoring and rendering audio reproduction data are provided. Some such authoring tools allow audio reproduction data to be generalized for a wide variety of reproduction environments. Audio reproduction data may be authored by creating metadata for audio objects. The metadata may be created with reference to speaker zones. During the rendering process, the audio reproduction data may be reproduced according to the reproduction speaker layout of a particular reproduction environment.

Abstract: Systems, methods, and computer program products for audio processing based on convolutional neural network (CNN) are described. The CNN architecture may comprise a multi-scale input block and a multi-scale nested block. The multi-scale input block may be configured to receive input data and to generate a first downsampled input data set by downsampling the input data. The multi-scale nested block may comprise a first encoding layer configured to generate a first encoded data set by performing a convolution based on the input data. The multi-scale nested block may comprise a second encoding layer configured to generate a second encoded data set by performing a convolution based on the first downsampled input data set. Furthermore, the multi-scale nested block may comprise a first convolutional layer configured to generate a first output data set by upsampling the second encoded data set, concatenating the first encoded data set and the upsampled second encoded data set, and performing a convolution.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.