Apple Inc.

Raghavendra S Prabhu

Esge Brandt Andersen

Baptiste Pierre Paquier

Tom-Davy W Saux

Hanchi Chen

Andrew E Greenwood

Joshua D Atkins

Vasu Iyengar

Daniele Giacobello

Lalin S Theverapperuma

Jason Wung

Sarmad Aziz Malik

Ramin Pichevar

A method performed by an in-ear headphone. Coupled to the in-ear headphone is a first ear tip that is inserted into an ear canal of a user. The method obtains an audio signal from an audio source device paired with the in-ear headphone and uses the signal to drive a speaker of the headphone to output a sound into the ear canal. The method obtains a microphone signal that is responsive to the outputted sound. The method notifies the user to replace the first ear tip with a second ear tip in response to a parameter associated with the microphone signal being less than a threshold.

Acoustic detection of in-ear headphone fit

Method of speech enhancement using Neural Network-based combined signal starts with training neural network offline which includes: (i) exciting at least one accelerometer and at least one microphone using training accelerometer signal and training acoustic signal, respectively. The training accelerometer signal and the training acoustic signal are correlated during clean speech segments. Training neural network offline further includes (ii) selecting speech included in the training accelerometer signal and in the training acoustic signal, and (iii) spatially localizing the speech by setting a weight parameter in the neural network based on the selected speech included in the training accelerometer signal and in the training acoustic signal. The neural network that is trained offline is then used to generate a speech reference signal based on an accelerometer signal from the at least one accelerometer and an acoustic signal received from the at least one microphone. Other embodiments are described.

System and method for performing speech enhancement using a neural network-based combined symbol

An echo canceller can be arranged to receive an input signal and to receive a reference signal. The echo canceller can subtract a linear component of the reference signal from the input signal. A noise suppressor can suppress non-linear effects of the reference signal in the input signal in correspondence with a large number of selectable parameters. Such suppression can be provided on a frequency-by-frequency basis, with a unique set of tunable parameters selected for each frequency. A degree of suppression provided by the noise suppressor can correspond to an estimate of residual echo remaining after the one or more linear components of the reference signal have been subtracted from the input signal, to an estimated double-talk probability, and to an estimated signal-to-noise ratio of near-end speech in the input signal for each respective frequency. A speech recognizer can receive a processed input signal from the noise suppressor.

Voice quality enhancement techniques, speech recognition techniques, and related systems

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Redondo Beach, CA, United States of America

Raghavendra S Prabhu