Abstract: Systems and methods for preserving headphone rendering mode (HRM) in object clustering are described. In an embodiment, an object-based audio data processing system includes a processor configured to receive a plurality of audio objects, wherein an audio object of the plurality of audio objects is associated with respective object metadata that indicates respective spatial position information and an HRM; determine a plurality of cluster positions by applying an extended hybrid distance metric to a spatial coding algorithm to calculate a partial loudness for each of the audio objects; render the audio objects to the cluster positions to form a plurality of clusters by applying the extended hybrid distance metric to the spatial coding algorithm to calculate object-to-cluster gains; and transmit the clusters to a spatial reproduction system.

Abstract: A method of audio processing includes classifying an audio signal as noise or as non-noise using a first model. For a noise signal. the audio signal is classified as user-generated content (UGC) noise or as professionally-generated content (PGC) noise using a second model. For a non-noise signal or PGC noise. the audio signal is processed using a first audio processing process. For UGC noise. the audio signal is processed using a second audio processing process.

Abstract: Systems and methods for preserving headphone rendering mode (HRM) in object clustering are described. In an embodiment, an object-based audio data processing system includes a processor configured to receive a plurality of audio objects, wherein an audio object of the plurality of audio objects is associated with respective object metadata that indicates respective spatial position information and an HRM; determine a plurality of cluster positions by applying an extended hybrid distance metric to a spatial coding algorithm to calculate a partial loudness for each of the audio objects; render the audio objects to the cluster positions to form a plurality of clusters by applying the extended hybrid distance metric to the spatial coding algorithm to calculate object-to-cluster gains; and transmit the clusters to a spatial reproduction system.

Abstract: A method for clustering audio objects may involve identifying a plurality of audio objects, wherein each audio object of the plurality of audio objects is associated with respective metadata that indicates respective spatial position information and respective rendering metadata. The method may involve assigning audio objects of the plurality of audio objects to categories of rendering metadata of a plurality of categories of rendering metadata, wherein at least one category of rendering metadata comprises a plurality of types of rendering metadata to be preserved. The method may involve determining an allocation of a plurality of audio object clusters to each category of rendering metadata. The method may involve rendering audio objects of the plurality of audio objects to an allocated plurality of audio object clusters based on the metadata that indicates spatial position information and based on the assignments of the audio objects to the categories of rendering metadata.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The processing devices are configured to, in each of a plurality of iterations, at each of the processing devices, receive a respective plurality of input tokens. Executing the MoE layer further includes, at each of the processing devices, selecting one or more destination expert sub-models associated with the input tokens. Respective numbers k of expert sub-models selected differ across the iterations. At each of the processing devices, executing the MoE layer further includes conveying the input tokens to the one or more destination expert sub-models. Executing the MoE layer further includes generating one or more respective expert sub-model outputs at the one or more destination expert sub-models. Executing the MoE layer further includes generating and outputting an MoE layer output based on the one or more expert sub-model outputs.

Abstract: A computing system is provided, including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The MoE layer includes a plurality of expert sub-models that each have a respective plurality of parameter values. The MoE layer is configured to be switchable between a data parallel mode and an expert-data-model parallel mode without conveying the respective parameter values of the expert sub-models among the plurality of processing devices.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer included in an MoE model. The processing devices are configured to execute the MoE layer at least in part by, during a first collective communication phase between the processing devices, splitting each of a plurality of first input tensors along a first dimension to obtain first output tensors. Executing the MoE layer further includes processing the first output tensors at a respective a plurality of expert sub-models to obtain a plurality of second input tensors. Executing the MoE layer further includes, during a second collective communication phase between the processing devices, receiving the second input tensors from the expert sub-models and concatenating the second input tensors along the first dimension to obtain second output tensors. Executing the MoE layer further includes outputting the second output tensors as output of the MoE layer.

Abstract: A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer. The processing devices are configured to execute the MoE layer at least in part by receiving an input tensor including input tokens. Executing the MoE layer further includes computing a gating function output vector based on the input tensor and computing a sparse encoding of the input tensor and the gating function output vector. The sparse encoding indicates one or more destination expert sub-models. Executing the MoE layer further includes dispatching the input tensor for processing at the one or more destination expert sub-models, and further includes computing an expert output tensor. Executing the MoE layer further includes computing an MoE layer output at least in part by computing a sparse decoding of the expert output tensor. Executing the MoE layer further includes conveying the MoE layer output to an additional computing process.

Abstract: An apparatus and method of blind detection of binauralized audio. If the input content is detected as binaural, a second binauralization may be avoided. In this manner, the user experience avoids audio artifacts introduced by multiple binauralizations.

Abstract: A method for steering binauralization of audio is provided. The method comprises steps of: receiving (410) an audio input signal, calculating (430) a confidence value indicating a likelihood that a current audio frame of the audio input signal comprises binauralized audio; determining (450) a state signal based on the confidence value; determining (460) a steering signal, based on the first confidence value, the state signal and an energy value of the audio frame; and generating (470) an audio output signal with steered binauralization by processing the audio input signal according to the steering signal.

Abstract: The present invention relates to a method and device for processing a first and a second audio signal representing an input binaural audio signal acquired by a binaural recording device. The present invention further relates to a method for rendering a binaural audio signal on a speaker system. The method for processing a binaural signal comprising extracting audio information from the first audio signal, computing a band gain for reducing noise in the first audio signal and applying the band gains to respective frequency bands of the first audio signal in accordance with a dynamic scaling factor, to provide a first output audio signal. Wherein the dynamic scaling factor has a value between zero and one and is selected so as to reduce quality degradation for the first audio signal.

Abstract: A positive electrode composite material for a lithium ion secondary battery, and a positive electrode using the positive electrode composite material for a lithium ion secondary battery, a lithium ion secondary battery and a power consuming device are provided. The positive electrode composite material for a lithium ion secondary battery comprises a positive electrode active material and an alkali metal oxide as shown by formula AmGOn, wherein A represents at least one element selected from Na and K and optionally Li, G represents at least one element selected from Fe, Ni, Co, Mn, Ru, Ir, Sn, Cr, Nb, Mo, V and Ti, m is 1-6, and n is 1-4.

Abstract: A positive electrode composite material for a lithium ion secondary battery includes a positive electrode active material and a positive electrode additive. The positive electrode additive includes at least one of oxides, sulphides, peroxides, azides, or oxycarbides of sodium or potassium.

Abstract: A secondary battery includes a negative electrode plate and a positive electrode plate. The negative electrode plate includes a negative electrode active material. The positive electrode plate includes a positive electrode active material. The negative electrode active material is selected from one or more of a silicon-based material and a tin-based material. The positive electrode active material has a molecular formula of M1xMnyM2zOaAb. M1 represents one or two of Mg and Al. M2 represents one or two of Co and Ni. A represents one or more of N, P, and S. 0<x?3, 0<y?6, 0?z?6, 0<y+z?8, 0=<a?12, 0?b?12, and 0<a+b?15.

Abstract: An apparatus and method of blind detection of binauralized audio. If the input content is detected as binaural, a second binauralization may be avoided. In this manner, the user experience avoids audio artifacts introduced by multiple binauralizations.

Abstract: Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.

Abstract: A method for steering binauralization of audio is provided. The method comprises steps of: receiving (410) an audio input signal, calculating (430) a confidence value indicating a likelihood that a current audio frame of the audio input signal comprises binauralized audio; determining (450) a state signal based on the confidence value; determining (460) a steering signal, based on the first confidence value, the state signal and an energy value of the audio frame; and generating (470) an audio output signal with steered binauralization by processing the audio input signal according to the steering signal.

Abstract: Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.

Abstract: An apparatus and method of blind detection of binauralized audio. If the input content is detected as binaural, a second binauralization may be avoided. In this manner, the user experience avoids audio artifacts introduced by multiple binauralizations.

Abstract: An apparatus and method of blind detection of binauralized audio. If the input content is detected as binaural, a second binauralization may be avoided. In this manner, the user experience avoids audio artifacts introduced by multiple binauralizations.