Abstract: Methods of processing audio data relating to user generated content are described. One method includes obtaining the audio data; applying frame-wise audio enhancement to the audio data; generating metadata for the enhanced audio data, based on one or more processing parameters of the frame-wise audio enhancement; and outputting the enhanced audio data together with the metadata. Another method includes obtaining the audio data and metadata for the audio data, wherein the metadata comprises first metadata indicative of one or more processing parameters of a previous frame-wise audio enhancement of the audio data; applying restore processing to the audio data, using the one or more processing parameters, to at least partially reverse the previous frame-wise audio enhancement; and applying frame-wise audio enhancement or editing processing to the restored raw audio data. Further described are corresponding apparatus, programs, and computer-readable storage media.

Abstract: The present disclosure relates to a method and system (1) for suppressing wind noise. The method comprises obtaining an input audio signal (100, 100?) comprising a plurality of consecutive audio signal segments (101, 102, 103, 101?, 102?, 103?) and suppressing wind noise in the input audio signal with a wind noise suppressor module (20) to generate a wind noise reduced audio signal. The method further comprises sing a neural network (10) trained to predict a set of gains for reducing noise in the input audio signal (100, 100?) given samples of the input audio signal (100, 100?), wherein a noise reduced audio signal is formed by applying said set of gains to the input audio signal (100, 100?) and mixing the wind noise reduced audio signal and the noise reduced audio signal with a mixer (30) to obtain an output audio signal with suppressed wind noise.

Abstract: Disclosed herein are method, systems, and computer-program products for segmenting a binaural recording of speech into parts containing self-speech and parts containing external speech, and processing each category with different settings, to obtain an enhanced overall presentation. The segmentation is based on a combination of: i) feature-based frame-by-frame classification, and ii) detecting dissimilarity by statistical methods. The segmentation information is then used by a speech enhancement chain, where independent settings are used to process the self- and external speech parts.

Abstract: The present disclosure relates to a method and audio processing system (1) for performing source separation. The method comprises obtaining (S1) an audio signal (Sin) including a mixture of speech content and noise content, determining (S2a, S2b, S2c), from the audio signal, speech content (formula A), stationary noise content (formula C) and non-speech content (formula B). The stationary noise content (formula C) is a true subset of the non-speech content (formula B) and the method further comprises determining (S3), based on a difference between the stationary noise content (formula C) and the non-speech content (formula B) a non-stationary noise content formula D), obtaining (S5) a set of weighting factors and forming (S6) a processed audio signal based on a combination of the speech content (formula A), the stationary noise content (formula C), and the non-stationary noise content (formula D) weighted with their respective weighting factor.

Abstract: The present disclosure relates to a method for designing a processor (20) and a computer implemented neural network. The method comprises obtaining input data and corresponding ground truth target data and providing the input data to a processor (20) for outputting a first prediction of target data given the input data. The method further comprises providing the latent variables output by a processor module (21: 1, 21: 2, . . . 21: n?1) to a supervisor module (22: 1, 22: 2, 22: 3, . . . 22: n?1) which outputs a second prediction of target data based on latent variables and determining a first and second loss measure by comparing the predictions of target data with the ground truth target data. The method further comprises training the processor (20) and the supervisor module (22: 1, 22: 2, 22: 3, . . . 22: n?1) based on the first and second loss measure and adjusting the processor by at least one of removing, replacing and adding a processor module.

Abstract: A system for real-time monitoring of user-generated audio content for audio anomaly and a related method are disclosed. In some embodiments, the system is programmed to receive, in real time, audio data generated by a first mobile device, such as a smartphone. The system is programed to determine, in real time, whether an audio anomaly has occurred from the audio data. The system is programmed to cause, in real time, a presentation of an alert to a second mobile device, which could be the same smartphone, in response to detecting an occurrence of audio anomaly.

Abstract: A method for reverberation suppression may involve receiving an input audio signal. The method may involve calculating an initial reverberation suppression gain for the input audio signal for at least one frame of the input audio signal. The method may involve calculating at least one adjusted reverberation suppression gain, where the at least one adjusted reverberation suppression gain adjusts at least one of: 1) a reverberation suppression decay based on a reverberation intensity detected in the input audio signal; 2) gains applied to different frequency bands of the input audio signal based on an amount of room resonance detected in the input audio signal; or 3) a loudness of the input audio signal based on a direct part of the input audio signal. The method may involve generating an output audio signal by applying the at least one adjusted reverberation suppression gain to the input audio signal.

Abstract: Embodiments are disclosed for context aware audio processing. In an embodiment, an audio processing method comprises: receiving, with one or more sensors of a device, environment information about an audio recording captured by the device; detecting, with at least one processor of the device, a context of the audio recording based on the audio recording and the environment information; determining, with the at least one processor, a model based on the context; processing, with the at least one processor, the audio recording based on the model to produce a processed audio recording with suppressed noise; determining, with the at least one processor, an audio processing profile based on the context; and combining, with the at least one processor, the audio recording and the processed audio recording based on the audio processing profile.

Abstract: A method for reverberation suppression may involve receiving an input audio signal. The method may involve classifying a media type of the input audio signal as one of a group comprising at least: 1) speech; 2) music; or 3) speech over music. The method may involve determining whether to perform dereverberation on the input audio signal based at least on a determination that the media type of the input audio signal has been classified as speech. The method may involve generating an output audio signal by performing dereverberation on the input audio signal in response to determining that dereverberation is to be performed on the input audio signal.

Abstract: Embodiments are disclosed for context aware soundscape control. In an embodiment, an audio processing method comprises: capturing, using a first set of microphones on a mobile device, a first audio signal from an audio scene; capturing, using a second set of microphones on a pair of earbuds, a second audio signal from the audio scene; capturing, using a camera on the mobile device, a video signal from a video scene; generating, with at least one processor, a processed audio signal from the first audio signal and the second audio signal, the processed audio signal generated with adaptive soundscape control based on context information; and combining, with the at least one processor, the processed audio signal and the captured video signal as multimedia output.

Abstract: A method of audio processing includes capturing a binaural audio signal, calculating noise reduction gains using a machine learning model, and generating a modified binaural audio signal. The method may further including performing various corrections to the audio to account for video captured by different cameras such as a front camera and a rear camera. The method may further include performing smooth switching of the binaural audio when switching between the front camera and the rear camera. In this manner, noise may be reduced in the binaural audio, and the user perception of the combined video and binaural audio may be improved.

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 method is disclosed herein for adapting parameters of a sibilance detector. Time-frequency features are extracted from an audio signal being received and. Based on those time-frequency features, a determination is made of whether the audio signal includes a short-term feature or a long-term feature. In accordance with determining that the audio signal includes the short-term feature or the long-term feature, one or more parameters of a sibilance detector for detecting sibilance in the audio signal are adapted. Sibilance in the audio signal, is detected using the sibilance detector with the one or more adapted parameters.

Abstract: The present application relates to a method, system, and computer program product of dynamically adjusting thresholds of a compressor responsive to an input audio signal. A scene switch analyzer receives an input audio signal having a plurality of frequency band components. The scene switch analyzer determines whether a scene switch has occurred in the input audio signal. The frequency band components of the input audio signal are processed. In response to determine that scene switch has not occurred, a distortion audibility system applies slow smoothing to compressor thresholds of the frequency band components. In response to determine that scene switch has occurred, the distortion audibility system applies fast smoothing or no smoothing to the compressor thresholds of the frequency band components.

Abstract: The present application relates to a method, system, and computer program product of dynamically adjusting thresholds of a compressor responsive to an input audio signal. A scene switch analyzer receives an input audio signal having a plurality of frequency band components. The scene switch analyzer determines whether a scene switch has occurred in the input audio signal. The frequency band components of the input audio signal are processed. In response to determine that scene switch has not occurred, a distortion audibility system applies slow smoothing to compressor thresholds of the frequency band components. In response to determine that scene switch has occurred, the distortion audibility system applies fast smoothing or no smoothing to the compressor thresholds of the frequency band components.