Aspects disclosed herein generally related to a method and system for efficient blind source separation using a topological approach. The method and system comprise locating and separating the audio streams by constructing and simplifying contour tree in a built time-frequency smooth weighted histogram in the subsystems included. Thus, in one example, the audio streams can be separated and reproduced in a faster, more reliability, higher quality and more robust way.

Aspects disclosed herein generally related to a method and system for efficient blind source separation using a topological approach. The method and system comprise locating and separating the audio streams by constructing and simplifying contour tree in a built time-frequency smooth weighted histogram in the subsystems included. Thus, in one example, the audio streams can be separated and reproduced in a faster, more reliability, higher quality and more robust way.

A voice conversion method and a related device are provided to implement diversified human voice beautification. A method in embodiments of this application includes: receiving a mode selection operation input by a user, where the mode selection operation is for selecting a voice conversion mode. A plurality of provided selectable modes include: a style conversion mode, for performing speaking style conversion on a to-be-converted first voice; a dialect conversion mode, for adding an accent to or removing an accent from the first voice; and a voice enhancement mode, for implementing voice enhancement on the first voice. The three modes have corresponding voice conversion networks. Based on a target conversion mode selected by the user, a target voice conversion network corresponding to the target conversion mode is selected to convert the first voice, and output a second voice obtained through conversion.

The present disclosure provides an authentication method, an authentication device, an electronic device and a storage medium. The authentication method includes: receiving target voice data; obtaining a first voiceprint feature parameter corresponding to the target voice data from a device voiceprint model library; performing a first encryption process on the first voiceprint feature parameter with a locally stored private key to generate to-be-verified data; transmitting the to-be-verified data to a server, so that the server uses a public key which matches the private key to decrypt the to-be-verified data to obtain the first voiceprint feature parameter, and performs authentication on the first voiceprint feature parameter to obtain an authentication result; receiving the authentication result returned by the server.

The present disclosure provides an authentication method, an authentication device, an electronic device and a storage medium. The authentication method includes: receiving target voice data; obtaining a first voiceprint feature parameter corresponding to the target voice data from a device voiceprint model library; performing a first encryption process on the first voiceprint feature parameter with a locally stored private key to generate to-be-verified data; transmitting the to-be-verified data to a server, so that the server uses a public key which matches the private key to decrypt the to-be-verified data to obtain the first voiceprint feature parameter, and performs authentication on the first voiceprint feature parameter to obtain an authentication result; receiving the authentication result returned by the server.

Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Methods and systems of authoring audio signal(s) into haptic data file(s) are disclosed. An audio analysis module analyses the audio signal(s) using filterbank(s) or by performing a spectrogram analysis. Transients are detected in the audio signal. If present, the transients are processed to determine a transient score and a transient binary. A database stores device specific information and actuator specific information. A haptic perceptual bandwidth of an electronic computing device having an embedded actuator is determined by using information from the database. A user interface allows modification of time-amplitude values and transient values based on the determined haptic perceptual bandwidth. Authored time amplitude values are aggregated in authored audio descriptor data, which is passed to a transformation module that fits the data into the haptic perceptual bandwidth and implements algorithms to produce transformed audio descriptor data. Finally, the transformed audio descriptor data is converted to the haptic data file.

Methods and systems of authoring audio signal(s) into haptic data file(s) are disclosed. An audio analysis module analyses the audio signal(s) using filterbank(s) or by performing a spectrogram analysis. Transients are detected in the audio signal. If present, the transients are processed to determine a transient score and a transient binary. A database stores device specific information and actuator specific information. A haptic perceptual bandwidth of an electronic computing device having an embedded actuator is determined by using information from the database. A user interface allows modification of time-amplitude values and transient values based on the determined haptic perceptual bandwidth. Authored time amplitude values are aggregated in authored audio descriptor data, which is passed to a transformation module that fits the data into the haptic perceptual bandwidth and implements algorithms to produce transformed audio descriptor data. Finally, the transformed audio descriptor data is converted to the haptic data file.

Two-stage speech/music classification device and method classify an input sound signal and select a core encoder for encoding the sound signal. A first stage classifies the input sound signal into one of a number of final classes. A second stage extracts high-level features of the input sound signal and selects the core encoder for encoding the input sound signal in response to the extracted high-level features and the final class selected in the first stage.

Two-stage speech/music classification device and method classify an input sound signal and select a core encoder for encoding the sound signal. A first stage classifies the input sound signal into one of a number of final classes. A second stage extracts high-level features of the input sound signal and selects the core encoder for encoding the input sound signal in response to the extracted high-level features and the final class selected in the first stage.