公开(公告)号：US11341945B2

公开(公告)日：2022-05-24

申请号：US16704600

申请日：2019-12-05

Applicant: Samsung Electronics Co., Ltd.

Inventor： Peter M. Bretan ,  Larry P. Heck ,  Hongxia Jin

IPC: G10H1/00 ,  G06N3/04 ,  G06K9/62 ,  G06F16/638

Abstract: A method includes receiving a non-linguistic input associated with an input musical content. The method also includes, using a model that embeds multiple musical features describing different musical content and relationships between the different musical content in a latent space, identifying one or more embeddings based on the input musical content. The method further includes at least one of: (i) identifying stored musical content based on the one or more identified embeddings or (ii) generating derived musical content based on the one or more identified embeddings. In addition, the method includes presenting at least one of: the stored musical content or the derived musical content. The model is generated by training a machine learning system having one or more first neural network components and one or more second neural network components such that embeddings of the musical features in the latent space have a predefined distribution.

公开(公告)号：US20210049989A1

公开(公告)日：2021-02-18

申请号：US16704600

申请日：2019-12-05

Applicant: Samsung Electronics Co., Ltd.

Inventor： Peter M. Bretan ,  Larry P. Heck ,  Hongxia Jin

IPC: G10H1/00 ,  G06N3/04 ,  G06K9/62 ,  G06F16/638

Abstract: A method includes receiving a non-linguistic input associated with an input musical content. The method also includes, using a model that embeds multiple musical features describing different musical content and relationships between the different musical content in a latent space, identifying one or more embeddings based on the input musical content. The method further includes at least one of: (i) identifying stored musical content based on the one or more identified embeddings or (ii) generating derived musical content based on the one or more identified embeddings. In addition, the method includes presenting at least one of: the stored musical content or the derived musical content. The model is generated by training a machine learning system having one or more first neural network components and one or more second neural network components such that embeddings of the musical features in the latent space have a predefined distribution.