Hearing device comprising a speech intelligibility estimator

    公开(公告)号:US11950057B2

    公开(公告)日:2024-04-02

    申请号:US17840172

    申请日:2022-06-14

    Applicant: Oticon A/S

    CPC classification number: H04R25/507 G10L25/78 G10L2025/786 H04R2225/41

    Abstract: A hearing device, e.g. a hearing aid, comprises a) an input unit configured to provide at least one time-variant electric input signal representing sound, the at least one electric input signal comprising target signal components and optionally noise signal components, the target signal components originating from a target sound source; b) a signal processing unit for processing the at least one electric input signal and providing a processed signal; c) an output unit for creating output stimuli configured to be perceivable by the user as sound based on the processed signal from the signal processing unit; d) a speech presence probability prediction unit for repeatedly providing a measure of a predicted speech presence probability of the at least one electric input signal, or of a signal originating therefrom; and e) a speech intelligibility prediction unit for repeatedly providing a current measure of a predicted speech intelligibility of the at least one electric input signal, or of a signal originating therefrom. The speech intelligibility prediction unit is configured to determine said current measure of the predicted speech intelligibility in dependence of said measure of the predicted speech presence probability. A method of operating a hearing device is further disclosed. The invention may e.g. be used in hearing aids, headsets, earpieces (ear buds), etc.

    Hearing device comprising a recurrent neural network and a method of processing an audio signal

    公开(公告)号:US11696079B2

    公开(公告)日:2023-07-04

    申请号:US17580493

    申请日:2022-01-20

    Applicant: Oticon A/S

    CPC classification number: H04R25/453 H04R25/405

    Abstract: A hearing device, e.g. a hearing aid or a headset, configured to be worn by a comprises an input unit for providing at least one electric input signal in a time-frequency representation; and a signal processor comprising a target signal estimator for providing an estimate of the target signal; a noise estimator for providing an estimate of the noise; and a gain estimator for providing respective gain values in dependence of said target signal estimate and said noise estimate. The gain estimator comprises a trained neural network, wherein the outputs of the neural network comprise real or complex valued gains, or separate real valued gains and real valued phases. The signal processor is configured—at a given time instance t—to calculate changes Δx(i,t)=x(i,t)−{circumflex over (x)}(i,t−1), and Δh(j,t−1)=h(j,t−1)−ĥ(j,t−2) to an input vector x(t) and to the hidden state vector h(t−1), respectively, from one time instance, t−1, to the next, t, and where {circumflex over (x)}(i,t−1) and ĥ(j,t−2) are estimated values of x(i,t−1) and h(j,t−2), respectively, where indices i, j refers to the ith input neuron and the jth neuron of the hidden state, respectively, where 1≤i≤Nch,x and 1≤j≤Nch,oh, wherein Nch,x and Nch,oh is the number of processing channels of the input vector x and the hidden state vector h, respectively, and wherein the signal processor is further configured to provide that the number of updated channels among said Nch,x and said Nch,oh processing channels of the modified gated recurrent unit for said input vector x(t) and said hidden state vector h(t−1), respectively, at said given time instance t is limited to a number of peak values Np,x, and Np,oh, respectively, where Np,x is smaller than Nch,x, and Np,oh, is smaller than Nch,oh.

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