In an audio system, an audio signal is preprocessed to provide an input signal to a fast detector and a slow detector, the input signal comprising alert signals and ambient sounds. The slow detector determines the ambient sound level of the input signal which is output to an alert signal detector. The alert signal detector uses the ambient sound level to compute an adaptive threshold level using an adaptive threshold function. The fast detector determines the envelope level of the input signal which is output to the alert signal detector. The alert signal detector compares the envelope level to the adaptive threshold level to determine if an alert signal is present in the input signal. The adaptive threshold level varies depending on the ambient sound level of the input signal and the alert signal detection of the audio system automatically adapts to changing acoustic environments having different ambient sound levels.

In some embodiments, a method for processing an audio signal in an audio processing apparatus is disclosed. The method includes receiving an audio signal and a parameter, the parameter indicating a location of an auditory event boundary. An audio portion between consecutive auditory event boundaries constitutes an auditory event. The method further includes applying a modification to the audio signal based in part on an occurrence of the auditory event. The parameter may be generated by monitoring a characteristic of the audio signal and identifying a change in the characteristic.

In some embodiments, a method for processing an audio signal in an audio processing apparatus is disclosed. The method includes receiving an audio signal and a parameter, the parameter indicating a location of an auditory event boundary. An audio portion between consecutive auditory event boundaries constitutes an auditory event. The method further includes applying a modification to the audio signal based in part on an occurrence of the auditory event. The parameter may be generated by monitoring a characteristic of the audio signal and identifying a change in the characteristic.

An audio watermark addition method is provided, and includes: A playback terminal obtains first audio in real time, embeds an audio watermark into the first audio, where the audio watermark is associated with the playback terminal; and plays the first audio embedded with the audio watermark.

An audio watermark addition method is provided, and includes: A playback terminal obtains first audio in real time, embeds an audio watermark into the first audio, where the audio watermark is associated with the playback terminal; and plays the first audio embedded with the audio watermark.

An ear-worn device includes: a microphone that obtains a sound and outputs a sound signal of the sound obtained; a DSP that performs signal processing on the sound signal to determine whether speech contained in the sound has reverberance, and outputs, based on a result of the determination, a first sound signal obtained by performing first signal processing on the sound signal; a loudspeaker that reproduces the sound based on the first sound signal output; and a housing that contains the microphone, the DSP, and the loudspeaker.

An ear-worn device includes: a microphone that obtains a sound and outputs a sound signal of the sound obtained; a DSP that performs signal processing on the sound signal to determine whether speech contained in the sound has reverberance, and outputs, based on a result of the determination, a first sound signal obtained by performing first signal processing on the sound signal; a loudspeaker that reproduces the sound based on the first sound signal output; and a housing that contains the microphone, the DSP, and the loudspeaker.

Provided is a device performing machine learning by extracting an acoustic feature value from conversational voice data and predicting a disease level of a subject on the basis of a disease prediction model to be generated by the machine learning, the device including: a matrix calculation unit 23 calculating a spatial delay matrix using a relation value of a plurality of types of acoustic feature values; and a matrix decomposition unit 24 calculating a matrix decomposition value from the spatial delay matrix, in which a relation value reflecting a non-linear and non-stationary relationship of the feature values can be obtained by calculating at least one of a DCCA coefficient and a mutual information amount as the relation value of the plurality of types of acoustic feature values, and the disease level of the subject can be predicted on the basis of the relation value.

Provided are a wearable device for providing a multi-modality, and an operation method of the wearable device. The operation method of the wearable device including obtaining source data including at least one of image data, text data, or sound data, determining whether the image data, the text data, and the sound data are included in the source data, based on determining that at least one of the image data, the text data, or the sound data is not included in the source data, generating the image data, the text data, and the sound data, which are not included in the source data, by using a generator of an generative adversarial network (GAN), which receives the source data as an input, generating a pulse-width modulation (PWM) signal based on the sound data, and outputting the multi-modality based on the image data, the text data, the sound data, and the PWM signal.

Provided are a wearable device for providing a multi-modality, and an operation method of the wearable device. The operation method of the wearable device including obtaining source data including at least one of image data, text data, or sound data, determining whether the image data, the text data, and the sound data are included in the source data, based on determining that at least one of the image data, the text data, or the sound data is not included in the source data, generating the image data, the text data, and the sound data, which are not included in the source data, by using a generator of an generative adversarial network (GAN), which receives the source data as an input, generating a pulse-width modulation (PWM) signal based on the sound data, and outputting the multi-modality based on the image data, the text data, the sound data, and the PWM signal.