5 results about "Sound separation" patented technology

The application discloses a mechanical arm deception attack side channel detection method based on acoustic characteristics and belongs to the technical field of deep learning, which comprises the following steps: constructing a training recognition model; receiving the collected target audio, performing a sound processing step operation to extract features, performing a training recognition step operation to output predicted motion data; comparing the predicted motion data with the obtained corresponding real-time motion data, and evaluating whether the difference between each pair of parameters exceeds a preset threshold value. The application uses sound separation technology to separate mixed multi-axis sound into different individual axis sound, constructs an acoustic motion information recognition model of each motion joint through a collaborative physical and data-driven method, identifies the target sound according to the recognition model, obtains the predicted motion information of each axis, compares the predicted motion information with the collected real-time motion command, combines a threshold value to determine whether a deception attack is received, and realizes mechanical arm deception attack side channel detection.

This invention discloses a multi-target sleep monitoring method and device based on snoring separation. The method includes: constructing independent respiratory wave time-stamped sequences corresponding to each monitored object based on the radio frequency signal corresponding to the target monitoring area; constructing a mixed snoring time-stamped sequence corresponding to all monitored objects based on the mixed environmental audio signal corresponding to the target monitoring area; performing cross-modal correlation matching between the mixed snoring time-stamped sequence and each independent respiratory wave time-stamped sequence to determine the independent snoring time-stamped sequence corresponding to each monitored object; and performing sleep monitoring analysis on the corresponding monitored objects based on the independent respiratory wave time-stamped sequence and / or the independent snoring time-stamped sequence of the same monitored object to obtain the sleep monitoring results corresponding to each monitored object. This achieves accurate sleep monitoring analysis of multiple monitored objects in the same space, resulting in precise sleep monitoring analysis results.

The present disclosure provides a mixed audio processing method and device, a storage medium and an electronic device. The mixed audio processing method comprises: acquiring mixed audio, the mixed audio comprising a first sound and a second sound; performing band processing on the mixed audio to obtain audio sub-band data; processing the audio sub-band data using an audio intelligent separation model to obtain audio features; and performing sound reconstruction according to the audio features to generate separated first sound and / or second sound. The scheme provided by the present disclosure can automatically extract complex features and achieve accurate sound separation effect.

According to an aspect of the present disclosure there is provided a method for adversarial training of a separator (30) for universal sound separation of an audio mixture m of arbitrary sound sources Sk=1, . . . ,K, the method comprising: training a context-based discriminator (34) configured to provide a context-based loss cue based on a consideration of an input set of separated sound sources; and training the separator (30) to minimize a loss based on the context-based loss cue provided by the context-based discriminator (34); wherein training the context-based discriminator (34) comprises maximizing a loss based on a set of ground-truth sound sources and a fake set of separated sound sources, wherein the fake set of separated sound sources is sorted to match an order of the set of ground-truth sound sources, and wherein the fake set of separated sound sources comprises sources corresponding to separated sound sources estimated by the separator (30) and further comprises one or more ground-truth sound sources of the set of ground-truth sound sources.