CYPHER, LLC Patent applications |
Patent application number | Title | Published |
20160134984 | DETERMINING NOISE AND SOUND POWER LEVEL DIFFERENCES BETWEEN PRIMARY AND REFERENCE CHANNELS - A method for estimating a noise power level difference (NPLD) between a primary microphone and a reference microphone of an audio device includes obtaining primary and reference channels of an audio signal with primary and reference microphones of an audio device and estimating a noise magnitude of the reference channel of the audio signal to provide a noise variance estimate for one or more frequencies. A modelled probability density function (PDF) of a fast Fourier transform (FFT) coefficient of the primary channel of the audio signal is maximized to provide a NPLD between the noise variance estimate of the reference channel and a noise variance estimate of the primary channel. A modelled PDF of an FFT coefficient of the reference channel of the audio signal is maximized to provide a complex speech power level difference (SPLD) coefficient between the speech FFT coefficients of the primary and reference channel. A corrected noise magnitude of the reference channel is then calculated based on the noise variance estimate, the NPLD and the SPLD coefficient. | 05-12-2016 |
20160133272 | ADAPTIVE INTERCHANNEL DISCRIMINATIVE RESCALING FILTER - A method for adjusting a degree of filtering applied to an audio signal includes modeling a probability density function (PDF) of a fast Fourier transform (FFT) coefficient of a primary channel and reference channel of the audio signal; maximizing at least one of PDFs to provide a discriminative relevance difference (DRD) between a noise magnitude estimate of the reference channel and a noise magnitude estimate of the primary channel. The method further includes emphasizing the primary channel when the spectral magnitude of the primary channel is stronger than the spectral magnitude of the reference channel; and deemphasizing the primary channel when the spectral magnitude of the reference channel is stronger than the spectral magnitude of the primary channel. The emphasizing and deemphasizing includes computing a multiplicative rescaling factor and applying the multiplicative rescaling factor to a gain computed in a prior stage of a speech enhancement filter chain when there is a prior stage, and directly applying a gain when there is no prior stage. | 05-12-2016 |
20160093313 | NEURAL NETWORK VOICE ACTIVITY DETECTION EMPLOYING RUNNING RANGE NORMALIZATION - A “running range normalization” method includes computing running estimates of the range of values of features useful for voice activity detection (VAD) and normalizing the features by mapping them to a desired range. Running range normalization includes computation of running estimates of the minimum and maximum values of VAD features and normalizing the feature values by mapping the original range to a desired range. Smoothing coefficients are optionally selected to directionally bias a rate of change of at least one of the running estimates of the minimum and maximum values. The normalized VAD feature parameters are used to train a machine learning algorithm to detect voice activity and to use the trained machine learning algorithm to isolate or enhance the speech component of the audio data. | 03-31-2016 |