Disclosed is a hierarchical generated audio detection system, comprising an audio preprocessing module, a CQCC feature extraction module, a LFCC feature extraction module, a first-stage lightweight coarse-level detection model and a second-stage fine-level deep identification model; the audio preprocessing module preprocesses collected audio or video data to obtain an audio clip with a length not exceeding the limit; inputting the audio clip into CQCC feature extraction module and LFCC feature extraction module respectively to obtain CQCC feature and LFCC feature; inputting CQCC feature or LFCC feature into the first-stage lightweight coarse-level detection model for first-stage screening to screen out the first-stage real audio and the first-stage generated audio; inputting the CQCC feature or LFCC feature of the first-stage generated audio into the second-stage fine-level deep identification model to identify the second-stage real audio and the second-stage generated audio, and the second-stage generated audio is identified as generated audio.

An abnormality degree calculation system includes: a feature amount vector extraction unit configured to generate and output a feature amount vector from an input signal originating from vibration of a target device; an encoding unit configured to receive as an input a set composed of the feature amount vector and a device type vector representing a type of the target device and output an encoding vector; a decoding unit configured receive as an input the encoding vector and the device type vector and output a decoding vector; a learning unit configured to learn parameters of the neural networks of the encoding unit and the decoding unit; and an abnormality degree calculation unit configured to calculate a degree of abnormality defined as a function of the feature amount vector from the feature amount vector extraction unit, the encoding vector from the encoding unit, and the decoding vector from the decoding unit.

An abnormality degree calculation system includes: a feature amount vector extraction unit configured to generate and output a feature amount vector from an input signal originating from vibration of a target device; an encoding unit configured to receive as an input a set composed of the feature amount vector and a device type vector representing a type of the target device and output an encoding vector; a decoding unit configured receive as an input the encoding vector and the device type vector and output a decoding vector; a learning unit configured to learn parameters of the neural networks of the encoding unit and the decoding unit; and an abnormality degree calculation unit configured to calculate a degree of abnormality defined as a function of the feature amount vector from the feature amount vector extraction unit, the encoding vector from the encoding unit, and the decoding vector from the decoding unit.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for end to end speech conversion are disclosed. In one aspect, a method includes the actions of receiving first audio data of a first utterance of one or more first terms spoken by a user. The actions further include providing the first audio data as an input to a model that is configured to receive first given audio data in a first voice and output second given audio data in a synthesized voice without performing speech recognition on the first given audio data. The actions further include receiving second audio data of a second utterance of the one or more first terms spoken in the synthesized voice. The actions further include providing, for output, the second audio data of the second utterance of the one or more first terms spoken in the synthesized voice.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for end to end speech conversion are disclosed. In one aspect, a method includes the actions of receiving first audio data of a first utterance of one or more first terms spoken by a user. The actions further include providing the first audio data as an input to a model that is configured to receive first given audio data in a first voice and output second given audio data in a synthesized voice without performing speech recognition on the first given audio data. The actions further include receiving second audio data of a second utterance of the one or more first terms spoken in the synthesized voice. The actions further include providing, for output, the second audio data of the second utterance of the one or more first terms spoken in the synthesized voice.

An audio scene classifier classifies an audio input signal from an audio scene and includes a pre-processing neural network configured for pre-processing the audio input signal based on initial classification parameters to produce an initial signal classification, and a scene classifier neural network configured for processing the initial scene classification based on scene classification parameters to produce an audio scene classification output. The initial classification parameters reflect neural network training based on a first set of initial audio training data, and the scene classification parameters reflect neural network training on a second set of classification audio training data separate and different from the first set of initial audio training data. A hearing implant signal processor configured for processing the audio input signal and the audio scene classification output to generate the stimulation signals to the hearing implant for perception by the patient as sound.

A text-to-speech synthesis method includes receiving text, inputting the received text in a synthesizer that includes a prediction network configured to convert the received text into speech data having a speech attribute that includes emotion, intention, projection, pace, and/or accent, and outputting said speech data. The prediction network is obtained by obtaining a first sub-dataset and a second sub-dataset, where the first sub-dataset and the second sub-dataset each include audio samples and corresponding text, and the speech attribute of the audio samples of the second sub-dataset is more pronounced than the speech attribute of the audio samples of the first sub-dataset, training a first model using the first sub-dataset until a performance metric reaches a first predetermined value, training a second model by further training the first model using the second sub-dataset until the performance metric reaches a second predetermined value, and selecting one trained model as the prediction network.

Described herein is a method of determining parameters for a generative neural network for processing an audio signal, wherein the generative neural network includes an encoder stage mapping to a coded feature space and a decoder stage, each stage including a plurality of convolutional layers with one or more weight coefficients, the method comprising a plurality of cycles with sequential processes of: pruning the weight coefficients of either or both stages based on pruning control information, the pruning control information determining the number of weight coefficients that are pruned for respective convolutional layers; training the pruned generative neural network based on a set of training data; determining a loss for the trained and pruned generative neural network based on a loss function; and determining updated pruning control information based on the determined loss and a target loss. Further described are corresponding apparatus, programs, and computer-readable storage media.

Described herein is a method of determining parameters for a generative neural network for processing an audio signal, wherein the generative neural network includes an encoder stage mapping to a coded feature space and a decoder stage, each stage including a plurality of convolutional layers with one or more weight coefficients, the method comprising a plurality of cycles with sequential processes of: pruning the weight coefficients of either or both stages based on pruning control information, the pruning control information determining the number of weight coefficients that are pruned for respective convolutional layers; training the pruned generative neural network based on a set of training data; determining a loss for the trained and pruned generative neural network based on a loss function; and determining updated pruning control information based on the determined loss and a target loss. Further described are corresponding apparatus, programs, and computer-readable storage media.

The invention provides a method for real-time concealing errors in audio data packets. A Long Short-Term Memory (LSTM) neural network with a plurality of nodes is provided and pre-trained with audio data. A sequence of packets is received, each packet comprising a set of modified discrete cosine transform (MDCT) coefficients associated with a frame comprising time-domain samples of the audio signal. These MDCT coefficient data are applied to the LSTM neural network, and in case it is identified that a received packet is an erroneous packet, an output from the LSTM neural network is used to generate estimated MDCT co-efficients to provide a concealment packet to replace the erroneous packet. Preferably, the MDCT coefficients are normalized prior to applying to the LSTM neural network. This method can be performed in real-time. A low latency can be obtained and still with a high audio quality.