公开(公告)号：US20230409281A1

公开(公告)日：2023-12-21

申请号：US18335060

申请日：2023-06-14

Applicant: Spotify AB

Inventor： Michael Scibor ,  Thor Kell ,  Rachel Malia Bittner ,  Tristan Jehan

IPC: G06F3/16 ,  G06F16/683 ,  G06N3/048

CPC classification number: G06F3/165 ,  G06F16/683 ,  G06N3/048 ,  G10H2210/031 ,  G10H2210/091 ,  G10H2210/061

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.

公开(公告)号：US20210103422A1

公开(公告)日：2021-04-08

申请号：US16595404

申请日：2019-10-07

Applicant: SPOTIFY AB

Inventor： Michael Scibor ,  Thor Kell ,  Rachel Malia Bittner ,  Tristan Jehan

IPC: G06F3/16 ,  G06N3/04 ,  G06F16/683

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.

公开(公告)号：US11714594B2

公开(公告)日：2023-08-01

申请号：US16595404

申请日：2019-10-07

Applicant: SPOTIFY AB

Inventor： Michael Scibor ,  Thor Kell ,  Rachel Malia Bittner ,  Tristan Jehan

IPC: G06F3/16 ,  G06F16/683 ,  G06N3/048

CPC classification number: G06F3/165 ,  G06F16/683 ,  G06N3/048 ,  G10H2210/031 ,  G10H2210/061 ,  G10H2210/091

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.