Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

In general, techniques are described by which to correlate scene-based audio data for psychoacoustic audio coding. A device comprising a memory and one or more processors may be configured to perform the techniques. The memory may store a bitstream including a plurality of encoded correlated components of a soundfield represented by scene-based audio data. The one or more processors may perform psychoacoustic audio decoding with respect to one or more of the plurality of encoded correlated components to obtain a plurality of correlated components, and obtain, from the bitstream, an indication representative of how the one or more of the plurality of correlated components were reordered in the bitstream. The one or more processors may reorder, based on the indication, the plurality of correlated components to obtain a plurality of reordered components, and reconstruct, based on the plurality of reordered components, the scene-based audio data.

A method for rejecting inherent noise of a microphone arrangement that includes a first microphone and a second microphone. The first microphone generates a first microphone signal from an ambient sound signal and the second microphone generates a second microphone signal from the ambient sound signal. A measure of correlation between the first microphone signal and the second microphone signal is ascertained, and inherent noise of the first microphone and/or of the second microphone in the first or second microphone signal is rejected on the basis of the measure of correlation.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

A method is used for detecting whether a device is being worn, when the device comprises a first transducer and a second transducer. It is determined when a signal detected by at least one of the first and second transducers represents speech. It is then determined when said speech contains speech of a first acoustic class and speech of a second acoustic class. A first correlation signal is generated, representing a correlation between signals generated by the first and second transducers during at least one period when said speech contains speech of the first acoustic class. A second correlation signal is generated, representing a correlation between signals generated by the first and second transducers during at least one period when said speech contains speech of the second acoustic class. It is then determined from the first correlation signal and the second correlation signal whether the device is being worn.

A method is used for detecting whether a device is being worn, when the device comprises a first transducer and a second transducer. It is determined when a signal detected by at least one of the first and second transducers represents speech. It is then determined when said speech contains speech of a first acoustic class and speech of a second acoustic class. A first correlation signal is generated, representing a correlation between signals generated by the first and second transducers during at least one period when said speech contains speech of the first acoustic class. A second correlation signal is generated, representing a correlation between signals generated by the first and second transducers during at least one period when said speech contains speech of the second acoustic class. It is then determined from the first correlation signal and the second correlation signal whether the device is being worn.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O( ) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.