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公开(公告)号：US20240282294A1
公开(公告)日：2024-08-22
申请号：US18651296
申请日：2024-04-30
Applicant: Google LLC
Inventor： Qingqing Huang , Daniel Sung-Joon Park , Aren Jansen , Timo Immanuel Denk , Yue Li , Ravi Ganti , Dan Ellis , Tao Wang , Wei Han , Joonseok Lee
CPC classification number: G10L15/063 , G10L15/16
Abstract: A corpus of textual data is generated with a machine-learned text generation model. The corpus of textual data includes a plurality of sentences. Each sentence is descriptive of a type of audio. For each of a plurality of audio recordings, the audio recording is processed with a machine-learned audio classification model to obtain training data including the audio recording and one or more sentences of the plurality of sentences closest to the audio recording within a joint audio-text embedding space of the machine-learned audio classification model. The sentence(s) are processed with a machine-learned generation model to obtain an intermediate representation of the one or more sentences. The intermediate representation is processed with a machine-learned cascaded diffusion model to obtain audio data. The machine-learned cascaded diffusion model is trained based on a difference between the audio data and the audio recording.
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公开(公告)号：US20240079001A1
公开(公告)日：2024-03-07
申请号：US18463196
申请日：2023-09-07
Applicant: Google LLC
Inventor： Andrea Agostinelli , Timo Immanuel Denk , Antoine Caillon , Neil Zeghidour , Jesse Engel , Mauro Verzetti , Christian Frank , Zalán Borsos , Matthew Sharifi , Adam Joseph Roberts
CPC classification number: G10L15/16 , G10H1/0008 , G10L15/063 , G10L15/1815 , G10H2210/056 , G10H2250/311
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.
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公开(公告)号：US11915689B1
公开(公告)日：2024-02-27
申请号：US18463196
申请日：2023-09-07
Applicant: Google LLC
Inventor： Andrea Agostinelli , Timo Immanuel Denk , Antoine Caillon , Neil Zeghidour , Jesse Engel , Mauro Verzetti , Christian Frank , Zalán Borsos , Matthew Sharifi , Adam Joseph Roberts , Marco Tagliasacchi
CPC classification number: G10L15/16 , G10H1/0008 , G10L15/063 , G10L15/1815 , G10H2210/056 , G10H2250/311
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.
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公开(公告)号：US20240233713A1
公开(公告)日：2024-07-11
申请号：US18412394
申请日：2024-01-12
Applicant: Google LLC
Inventor： Andrea Agostinelli , Timo Immanuel Denk , Antoine Caillon , Neil Zeghidour , Jesse Engel , Mauro Verzetti , Christian Frank , Zalán Borsos , Matthew Sharifi , Adam Joseph Roberts , Marco Tagliasacchi
IPC: G10L15/16 , G06N3/0455 , G06N3/0475 , G10H1/00 , G10L15/06 , G10L15/18
CPC classification number: G10L15/16 , G06N3/0455 , G06N3/0475 , G10H1/0008 , G10L15/063 , G10L15/1815 , G10H2210/056 , G10H2250/311
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.
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