Abstract: Machine learning is used to classify a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. Differences between each pleasantness rating and each of the other pleasantness ratings is determined via pairwise comparisons. These differences are converted into binary values based on which pleasantness rating is higher or lower in each comparison. Measurable sound qualities are received associated with the sounds. Second differences between each of the measurable sound qualities and every other of the plurality of measured sound qualities is determined in pairwise fashion. A classification model is trained to classify sound pleasantness by comparing the binary values with the second differences.

Abstract: Machine learning is used to predict a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. A microphone system detects a plurality of measurable sound qualities (e.g., loudness, tonality, sharpness, etc.) of these rated sounds. A regression prediction model is trained based on the jury pleasantness ratings and the corresponding measurable sound qualities. Then, the microphone system detects measurable sound qualities of an unrated sound that has not been rated by the jury. The trained regression prediction model is executed on the measurable sound quality of the unrated sound to yield a predicted pleasantness of the unrated sound.

Abstract: A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.

Abstract: A system includes a processor, wherein the processor is programmed to receive sound information and vibrational information from a device in a first environment, generate a training data set utilizing at least the vibrational information and a sound perception score associated with the corresponding sound of the vibrational information, wherein the training data set is fed into an un-trained machine learning model, in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model, receive real-time vibrational information from the device in a second environment, and based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound perception score indicating characteristics associated with sound emitted from the device.

Abstract: A method includes, in response to at least one convergence criterion not being met: receiving a labeled dataset that includes a plurality of labeled samples; receiving an unlabeled dataset that includes a plurality of unlabeled samples; identifying a plurality of labeled-unlabeled sample pairs; applying a data augmentation transformation to each labeled sample and each corresponding unlabeled sample; computing, for each least one labeled-unlabeled sample pair, latent representation spaces using the machine learning model; generating, using the machine learning model, a label prediction for each unlabeled sample for each labeled-unlabeled sample pair; computing a loss function for each labeled-unlabeled sample pair of the plurality of labeled-unlabeled sample pairs based on respective latency representation spaces and respective label predictions; applying an optimization function to each respective loss function; and updating a weight value for each labeled-unlabeled sample pair of the plurality of labeled-un

Abstract: A filter device, in particular, for high-pressure applications in a molten polymer filtration, has a screw-in piece (10), provided with a first threaded section (12) for fixing the filter device, and a second threaded section (14) for fixing a filter medium (16), in particular, in the form of a metal wool and with a perforated support tube (18) for the metal wool. The other free end of the filter device is provided with a volume displacer (24). The metal wool is surrounded by a support grid (20) and fixed by two fixing pieces (28, 30), held at a separation from each other by the support grid (20). One fixing piece (28) is fixed to the second threaded section (14) of the screw-in piece (10). The other fixing piece forms a handle (30) for the fixing formed.

Abstract: The invention relates to a filter device, in particular for high-pressure applications in a molten polymer filtration, with a screw-in piece (10), provided with a first threaded section (12), for fixing the filter device, a second threaded section (14), for fixing a filter medium (16), in particular in the form of a metal wool and with a perforated support tube (18) for the metal wool. The other free end thereof is provided with a volume displacer (24), whereby the metal wool is surrounded by a support grid (20) and fixed by two fixing pieces (28, 30), held at a separation from each other by means of the support grid (20), one of which (28) serves for the fixing to the second threaded section (14) of the screw-in piece (10) and the other as a handle (30) for the fixing thus carried out.