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. (Ŝ1) formula A ({circumflex over (N)}1) formula B ({circumflex over (N)}2) formula C ({circumflex over (N)}NS) formula D
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:
Volume leveling of an audio signal using a volume leveling control signal. The method comprises determining a noise reliability ratio w(n) as a ratio of noise-like frames over all frames in a current time segment, determining a PGC noise confidence score XPGN(n) indicating a likelihood that professionally generated content, PGC, noise is present in the time segment, and determining, for the time segment, whether the noise reliability ratio is above a predetermined threshold. When the noise reliability ratio is above the predetermined threshold, the volume leveling control signal is updated based on the PGC noise confidence score, and when the noise reliability ratio is below the predetermined threshold, the volume leveling control signal is left unchanged. Volume leveling is improved by preventing boosting of e.g. phone-recorded environmental noise in UGC, while keeping original behavior for other types of content.
Abstract:
Described herein is a method for Convolutional Neural Network (CNN) based speech source separation, wherein the method includes the steps of: (a) providing multiple frames of a time-frequency transform of an original noisy speech signal; (b) inputting the time-frequency transform of said multiple frames into an aggregated multi-scale CNN having a plurality of parallel convolution paths; (c) extracting and outputting, by each parallel convolution path, features from the input time-frequency transform of said multiple frames; (d) obtaining an aggregated output of the outputs of the parallel convolution paths; and (e) generating an output mask for extracting speech from the original noisy speech signal based on the aggregated output. Described herein are further an apparatus for CNN based speech source separation 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:
In some embodiments, virtualization methods for generating a binaural signal in response to channels of a multi-channel audio signal, which apply a binaural room impulse response (BRIR) to each channel including by using at least one feedback delay network (FDN) to apply a common late reverberation to a downmix of the channels. In some embodiments, input signal channels are processed in a first processing path to apply to each channel a direct response and early reflection portion of a single-channel BRIR for the channel, and the downmix of the channels is processed in a second processing path including at least one FDN which applies the common late reverberation. Typically, the common late reverberation emulates collective macro attributes of late reverberation portions of at least some of the single-channel BRIRs. Other aspects are headphone virtualizers configured to perform any embodiment of the method.
Abstract:
In some embodiments, virtualization methods for generating a binaural signal in response to channels of a multi-channel audio signal, which apply a binaural room impulse response (BRIR) to each channel including by using at least one feedback delay network (FDN) to apply a common late reverberation to a downmix of the channels. In some embodiments, input signal channels are processed in a first processing path to apply to each channel a direct response and early reflection portion of a single-channel BRIR for the channel, and the downmix of the channels is processed in a second processing path including at least one FDN which applies the common late reverberation. Typically, the common late reverberation emulates collective macro attributes of late reverberation portions of at least some of the single-channel BRIRs. Other aspects are headphone virtualizers configured to perform any embodiment of the method.
Abstract:
The present disclosure relates to reverberation generation for headphone virtualization. A method of generating one or more components of a binaural room impulse response (BRIR) for headphone virtualization is described. In the method, directionally-controlled reflections are generated, wherein directionally-controlled reflections impart a desired perceptual cue to an audio input signal corresponding to a sound source location. Then at least the generated reflections are combined to obtain the one or more components of the BRIR. Corresponding system and computer program products are described as well.
Abstract:
Embodiments of the present invention relate to adaptive audio content generation. Specifically, a method for generating adaptive audio content is provided. The method comprises extracting at least one audio object from channel-based source audio content, and generating the adaptive audio content at least partially based on the at least one audio object. Corresponding system and computer program product are also disclosed.
Abstract:
Methods and apparatus for improving noise compensation in mask-based speech enhancement are described. A method of processing an audio signal, which includes one or more speech segments, includes obtaining a mask for mask-based speech enhancement of the audio signal and obtaining a magnitude of the audio signal. An estimate of residual noise is determined in the audio signal after mask-based speech enhancement, based on the mask and the magnitude of the audio signal. A modified mask is determined based on the estimate of the residual noise. Further described are corresponding programs and computer-readable storage media.
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.