Abstract: A method of training a machine learning model to conduct authentication transactions is provided that includes the steps of obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and information for a passphrase spoken by the respective user and belongs to a same or different data class. Each data class includes a user identity and a passphrase identifier. Moreover, the method includes the steps of creating, using a machine learning model being trained, at least one embedding for each audio signal. The machine learning model includes parameters. Furthermore, the method includes calculating, by a machine learning algorithm using the embeddings, a loss, and updating parameters of the machine learning model based on the calculated loss.

Abstract: A method of training a machine learning model (MLM) to conduct authentication transactions includes obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and an audio data production flag and belongs to a same or different data class. Moreover, the method includes creating, using a MLM being trained, at least one embedding for each audio signal and calculating, using the at least one embedding, a loss for a machine learning algorithm used for training the MLM. Furthermore, the method includes updating parameters of the MLM based on the calculated loss. In response to determining criteria defining an end of training have been satisfied, deeming the MLM to be operable for use in simultaneously successfully verifying the identity of a user based on voice biometric data and verifying the voice biometric data was spoken by a living person.

Abstract: The invention relates to a method for broadcasting a spatialized audio stream to terminals of spectators attending a sports event. The method comprises the acquisition of a plurality of audio streams constituting a soundscape. The soundscape is analyzed by a server in order for the sound spatialization of the audio streams and of the playback thereof on terminals, depending both on the localization of the audio flows and also the position of the spectators.

Abstract: The invention relates to a method for broadcasting a spatialized audio stream to terminals (TERM) of spectators attending a sports event. The method comprises the acquisition of a plurality of audio streams constituting a soundscape (SS). The soundscape (SS) is analyzed by a server (SERV) in order for the sound spatialization of the audio streams and of the playback thereof on terminals (TERM), depending both on the localization of the audio flows and also the position of the spectators.

Abstract: The invention relates to data processing for sound playing on a sound playing device (DIS), headset or ear bud type, portable by a user in an environment (ENV). The device comprises at least one speaker (HP), at least one microphone (MIC), and a connection to a processing circuit comprising: An input interface (IN) for receiving signals coming at least from the microphone; A processing unit (PROC, MEM) for reading at least one audio content to play on the speaker; and An output interface (OUT) for delivering at least the audio signals to be played by the speaker. The processing unit is arranged for: a) Analyzing the signals coming from the microphone for identifying sounds coming from the environment and corresponding to predetermined classes of target sounds; b) Selecting at least one identified sound, according to a user preference criterion; and c) Building said audio signals to be played by the speaker, by a selected mixing of the audio content and the selected sound.

Abstract: A method for determining user liveness is provided that includes calculating, by a computing device, a spectral property difference between voice biometric data captured from a user and user record voice biometric data. The user and the computing device constitute a user-computing device pair, and the voice biometric data is captured by the computing device during a verification transaction. Moreover, the method includes inputting the spectral property difference into a machine learning algorithm, calculating an output score with the machine learning algorithm, and determining the voice biometric data was captured from a live user when the output score satisfies a threshold score.

Abstract: A method for determining user liveness is provided that includes calculating, by a computing device, a spectral property difference between voice biometric data captured from a user and user record voice biometric data. The user and the computing device constitute a user-computing device pair, and the voice biometric data is captured by the computing device during a verification transaction. Moreover, the method includes inputting the spectral property difference into a machine learning algorithm, calculating an output score with the machine learning algorithm, and determining the voice biometric data was captured from a live user when the output score satisfies a threshold score.

Abstract: A method for splitting a digital signal using prosodic features included in the signal is provided that includes calculating onset value locations in the signal. The onset values correspond to stress accents in the signal. Moreover, the method includes splitting, using a processor, the signal into a prosodic unit candidate sequence by superimposing the stress accent locations on the signal, and processing the sequence to include only true prosodic units.

Abstract: A method for splitting a digital signal using prosodic features included in the signal is provided that includes calculating onset value locations in the signal. The onset values correspond to stress accents in the signal. Moreover, the method includes splitting, using a processor, the signal into a prosodic unit candidate sequence by superimposing the stress accent locations on the signal, and processing the sequence to include only true prosodic units.

Abstract: A source separation system is provided. The system includes a plurality of sources being subjected to an automatic source separation via a joint use of segmental information and spatial diversity. The system further includes a set of spectral shapes representing spectral diversity derived from the automatic source separation being automatically provided. The system still further includes a plurality of mixing parameters derived from the set of spectral shapes. Within a sampling range, a triplet is processed wherein a reconstruction of a Short Term Fourier Transform (STFT) corresponding to a source triplet among the set of triplets is performed.

Abstract: A method is provided that comprises segmenting an audio source file; optimizing a model based upon probability; and separating the audio source file.

Abstract: Unsupervised learning algorithms for audio source separation such as non-negative matrix factorization (NMF) and principal components analysis (PCA) can be understood as a data matrix factorization subject to different constraints. These algorithms provide components with a relevant structure and homogeneous musical events. The invention presents an automatic fusion method to merge these components into tracks associated to the different instruments present in the sound source.