Abstract: A method of detecting occurrences of a sound event in an audio signal comprising a sequence of frames of audio data, each frame corresponding to a respective time in the audio signal, the method comprising: for each frame in the sequence: determining, using the audio data of the frame, a first conditional probability value that a transition occurred from a sound event not having started to the sound event being ongoing, and a second conditional probability value that a transition occurred from a sound event being ongoing to the sound event having ended; and determining a marginal probability value that a sound event was ongoing at the time corresponding to the frame, the marginal probability value being determined using the first and second conditional probability values for the frame and a previously determined marginal probability value that a sound event was ongoing at a time corresponding to a frame preceding the frame in the sequence.

Abstract: A method for recognising at least one of a non-verbal sound event and a scene in an audio signal comprising a sequence of frames of audio data, the method comprising: for each frame of the sequence: processing the frame of audio data to extract multiple acoustic features for the frame of audio data; and classifying the acoustic features to classify the frame by determining, for each of a set of sound classes, a score that the frame represents the sound class; processing the sound class scores for multiple frames of the sequence of frames to generate, for each frame, a sound class decision for each frame; and processing the sound class decisions for the sequence of frames to recognise the at least one of a non-verbal sound event and a scene.

Abstract: Verification of presence of a detected target is carried out following an initial presence determination on the basis of detected non-verbal sound.

Abstract: Control of navigation of a content item is carried out by detection of non-verbal sound events. On the basis of receipt of one or more non-verbal sound event reports, a navigation tool is provided with a corresponding sequence of navigation commands. The correspondence between navigation command sequences and non-verbal sound events is established through analysis or markup of the content item.

Abstract: A device or system is provided which is configured to detect one or more sound events and/or scenes associated with a predetermined context, and to provide an assistive output on fulfilment of that context.

Abstract: A device or system is provided which is configured to detect one or more sound events and/or scenes associa ted with a predetermined context, and to provide an assistive output on fulfilment of that context.

Abstract: A method for recognising at least one of a non-verbal sound event and a scene in an audio signal comprising a sequence of frames of audio data, the method comprising: for each frame of the sequence: processing the frame of audio data to extract multiple acoustic features for the frame of audio data; and classifying the acoustic features to classify the frame by determining, for each of a set of sound classes, a score that the frame represents the sound class; processing the sound class scores for multiple frames of the sequence of frames to generate, for each frame, a sound class decision for each frame; and processing the sound class decisions for the sequence of frames to recognise the at least one of a non-verbal sound event and a scene.

Abstract: Sound detection and identification leads to responsiveness within an augmented reality environment. Information about an identified sound can be converted into a command for implementation by an augmented reality system, for display of a desired on-screen augmented reality effect.

Abstract: Verification of presence of a detected target is carried out following an initial presence determination on the basis of detected non-verbal sound.

Abstract: Control of navigation of a content item is carried out by detection of non-verbal sound events. On the basis of receipt of one or more non-verbal sound event reports, a navigation tool is provided with a corresponding sequence of navigation commands. The correspondence between navigation command sequences and non-verbal sound events is established through analysis or markup of the content item.

Abstract: A method for recognising at least one of a non-verbal sound event and a scene in an audio signal comprising a sequence of frames of audio data, the method comprising: for each frame of the sequence: receiving at least one sound class score, wherein each sound class score is representative of a degree of affiliation of the frame with a sound class of a plurality of sound classes; for a sound class score of the at least one sound class scores: determining a confidence that the sound class score is representative of a degree of affiliation of the frame with the sound class by processing a value for a property associated with the frame, wherein the value is processed using a learned model for the property; adjusting the sound class score for the frame based at least on the determined confidence.

Abstract: A method of training a non-verbal sound class detection machine learning system, the non-verbal sound class detection machine learning system comprising a machine learning model configured to: receive data for each frame of a sequence of frames of audio data obtained from an audio signal; for each frame of the sequence of frames: process the data for multiple frames; and output data for at least one sound class score representative of a degree of affiliation of the frame with at least one sound class of a plurality of sound classes, wherein the plurality of sound classes comprises: one or more target sound classes; and a non-target sound class representative of an absence of each of the one or more target sound classes; wherein the method comprises: training the machine learning model using a loss function.

Abstract: In a particular implementation, a codebook C can be used for quantizing a feature vector of a database image into a quantization index, and then a different codebook (B) can be used to approximate the feature vector based on the quantization index. The codebooks B and C can have different sizes. Before performing image search, a lookup table can be built offline to include distances between the feature vector for a query image and codevectors in codebook B to speed up the image search. Using triplet constraints wherein a first image and a second image are indicated as a matching pair and the first image and a third image as non-matching, the codebooks B and C can be trained for the task of image search. The present principles can be applied to regular vector quantization, product quantization, and residual quantization.

Abstract: A method for performing audio inpainting, wherein missing portions in an input audio signal are recovered and a recovered audio signal is obtained, comprises computing a Short-Time Fourier Transform (STFT) on portions of the input audio signal, computing conditional expectations of the source power spectra of the input audio signal, wherein estimated source power spectra P(f, n, j) are obtained and wherein the variance tensor V and complex Short-Time Fourier Transform (STFT) coefficients of the input audio signals are used, iteratively re-calculating the variance tensor V from the estimated power spectra P(f, n, j) and re-calculating updated estimated power spectra P(f, n, j), computing an array of STFT coefficients ? from the resulting variance tensor V according to ?(f, n, j)=E{S(f, n, j)|x, Is, IL, V}, and converting the array of STFT coefficients ? to the time domain, wherein coefficients {tilde over (s)}1, {tilde over (s)}2, . . . , {tilde over (s)}j of the recovered audio signal are obtained.

Abstract: A method for encoding multiple audio signals comprises random sampling and quantizing each of the multiple audio signals, and encoding the sampled and quantized multiple audio signals as side information that can be used for decoding and separating the multiple audio signals from a mixture of said multiple audio signals. A method for decoding a mixture of multiple audio signals comprises decoding and demultiplexing side information, the side information comprising quantized samples of each of the multiple audio signals, receiving or retrieving from any data source a mixture of said multiple audio signals, and generating multiple estimated audio signals that approximate said multiple audio signals, wherein said quantized samples of each of the multiple audio signals are used.

Abstract: Adapting characteristics of a video stream. A video stream is received, at a video adaptation device, which employs a first reference frame. A determination is made, at a video adaptation device, to splice the video stream to employ a second reference frame in place of the first reference frame. The video stream is spliced, at a video adaptation device, to create a spliced video stream that employs the second reference frame.