Abstract: The embodiments of the disclosure provide a method for evaluating a health condition indicator of a subject, a host, and a computer readable storage medium. The method includes: obtaining a reference heart rate of the subject and determining a heart rate indicator of the subject according to the reference heart rate; determining a fitness indicator of the subject based on physiological information of the subject; determining an age indicator of the subject based on an age of the subject; and determining the health condition indicator of the subject at least based on the heart rate indicator, the fitness indicator, and the age indicator.

Abstract: The disclosure provides a wearing detection method, a wearable device, and a computer readable storage medium. The method includes: in response to determining that the wearable device is in a static state, emitting a plurality of first photoplethysmography (PPG) signals, and detecting a plurality of second PPG signals corresponding to the first PPG signals; obtaining a zero-crossing rate corresponding to the second PPG signals; and in response to determining that the zero-crossing rate of the second PPG signals is higher than a reference threshold, determining that the wearable device is in an unworn state.

Abstract: An exercise monitoring method, an exercise monitoring device, and a computer-readable storage medium are provided. The method includes the following: obtaining an exercise course input by a user; detecting a reference respiratory pattern of the user performing the exercise course through a wearable device within a reference time interval; detecting a first respiratory pattern of the user performing the exercise course through the wearable device within a first time interval; and providing a first exercise adjustment suggestion based on a first comparison result between the first respiratory pattern and the reference respiratory pattern.

Abstract: The embodiments of the disclosure provide a method for adjusting a noise cancellation mode, an electronic device, and a computer readable storage medium. The method includes: estimating a noise perception indicator corresponding to an environmental noise; and adjusting a noise cancellation mode of an wearable audio device according to the noise perception indicator.

Abstract: The disclosure provides a wearing detection method, a wearable device, and a computer readable storage medium. The method includes: in response to determining that the wearable device is in a static state, emitting a plurality of first photoplethysmography (PPG) signals, and detecting a plurality of second PPG signals corresponding to the first PPG signals; obtaining a zero-crossing rate corresponding to the second PPG signals; and in response to determining that the zero-crossing rate of the second PPG signals is higher than a reference threshold, determining that the wearable device is in an unworn state.

Abstract: A method, an electronic device, and a recording medium for compensating an in-ear audio signal are provided. The method is applicable to the electronic device having a processor. In the method, an in-ear audio signal transmitted through an inner ear when a user speaks is captured by using an in-ear microphone, and an outer audio signal transmitted through air when the user speaks is captured by using an outer microphone in a training stage. Then, a machine learning model of audio signals is established for an objective function and is trained by using the in-ear audio signal and the outer audio signal. Finally, the in-ear audio signal captured by the in-ear microphone is converted into a compensated audio signal by using the trained machine learning model in an online stage, and the compensated audio signal is output.

Abstract: A sound processing method includes the following steps: obtaining first audio data of a specific object that corresponds to a first position; when the specific object moves to a second position, calculating movement information of the specific object according to the first position and the second position; searching a space transfer database for a space transfer function that corresponds to the movement information; and applying the space transfer function to the first audio data, so that the specific object generates a sound output that corresponds to the second position.

Abstract: A method, an electronic device, and a recording medium for compensating an in-ear audio signal are provided. The method is applicable to the electronic device having a processor. In the method, an in-ear audio signal transmitted through an inner ear when a user speaks is captured by using an in-ear microphone, and an outer audio signal transmitted through air when the user speaks is captured by using an outer microphone in a training stage. Then, a machine learning model of audio signals is established for an objective function and is trained by using the in-ear audio signal and the outer audio signal. Finally, the in-ear audio signal captured by the in-ear microphone is converted into a compensated audio signal by using the trained machine learning model in an online stage, and the compensated audio signal is output.

Abstract: The present disclosure illustrates an acoustic echo cancellation method and system using the same. The acoustic echo cancellation method comprises the following steps. Firstly, a prior-knowledge matrix comprising a plurality of space vectors is built. Then, an initial filter vector is generated by the prior-knowledge matrix and an initial weighting vector. The weighting vector is updated based on the difference of the echo signal and the estimated signal in an iteration algorithm, and the coefficient of the filter vector is updated according to the updated weighting vector. An estimated signal is generated according to the updated filter vector and the original signal. Finally, the next echo signal is cancelled by the near-end estimated signal.

Abstract: The present disclosure illustrates an acoustic echo cancellation method and system using the same. The acoustic echo cancellation method comprises the following steps. Firstly, a prior-knowledge matrix comprising a plurality of space vectors is built. Then, an initial filter vector is generated by the prior-knowledge matrix and an initial weighting vector. The weighting vector is updated based on the difference of the echo signal and the estimated signal in an iteration algorithm, and the coefficient of the filter vector is updated according to the updated weighting vector. An estimated signal is generated according to the updated filter vector and the original signal. Finally, the next echo signal is cancelled by the near-end estimated signal.