MX2021005814A - Tecnicas para identificar errores de sincronizacion en titulos de medios. - Google Patents

Tecnicas para identificar errores de sincronizacion en titulos de medios.

Info

Publication number
MX2021005814A
MX2021005814A MX2021005814A MX2021005814A MX2021005814A MX 2021005814 A MX2021005814 A MX 2021005814A MX 2021005814 A MX2021005814 A MX 2021005814A MX 2021005814 A MX2021005814 A MX 2021005814A MX 2021005814 A MX2021005814 A MX 2021005814A
Authority
MX
Mexico
Prior art keywords
synchronization errors
media titles
neural network
media
techniques
Prior art date
Application number
MX2021005814A
Other languages
English (en)
Inventor
Rohit Puri
Naji Khosravan
Shervin Ardeshir Behrostaghi
Original Assignee
Netflix Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Netflix Inc filed Critical Netflix Inc
Publication of MX2021005814A publication Critical patent/MX2021005814A/es

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
    • G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/57—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for processing of video signals
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
    • G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
    • G11B27/10—Indexing; Addressing; Timing or synchronising; Measuring tape travel
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
    • G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
    • G11B27/36—Monitoring, i.e. supervising the progress of recording or reproducing

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Signal Processing (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Mathematical Physics (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Television Signal Processing For Recording (AREA)

Abstract

Un sistema de red neuronal que está entrenado para identificar una o más porciones de un título de medios en donde es probable que estén presentes errores de sincronización. El sistema de red neuronal se entrena con base en un primer conjunto de títulos de medios en donde están presentes errores de sincronización, y un segundo conjunto de títulos de medios en donde están ausentes errores de sincronización. El segundo conjunto de títulos de medios puede generarse al introducir errores de sincronización en un conjunto de títulos de medios que, de otro modo, carece de errores de sincronización. A través del entrenamiento, el sistema de red neuronal aprende a identificar características visuales específicas incluidas en uno o más cuadros de video, y características de audio correspondientes que deben ser reproducidas en sincronía con las características visuales asociadas. Como consecuencia, cuando se presenta con un título de medios que incluye errores de sincronización, la red neuronal puede indicar los cuadros específicos en donde es probable que haya errores de sincronización.
MX2021005814A 2018-11-19 2019-11-19 Tecnicas para identificar errores de sincronizacion en titulos de medios. MX2021005814A (es)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201862769515P 2018-11-19 2018-11-19
US16/687,209 US20200160889A1 (en) 2018-11-19 2019-11-18 Techniques for identifying synchronization errors in media titles
PCT/US2019/062240 WO2020106737A1 (en) 2018-11-19 2019-11-19 Techniques for identifying synchronization errors in media titles

Publications (1)

Publication Number Publication Date
MX2021005814A true MX2021005814A (es) 2021-07-02

Family

ID=70726714

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2021005814A MX2021005814A (es) 2018-11-19 2019-11-19 Tecnicas para identificar errores de sincronizacion en titulos de medios.

Country Status (7)

Country Link
US (1) US20200160889A1 (es)
EP (1) EP3884350A1 (es)
AU (1) AU2019384731B2 (es)
BR (1) BR112021009617A2 (es)
CA (1) CA3119042C (es)
MX (1) MX2021005814A (es)
WO (1) WO2020106737A1 (es)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11694084B2 (en) 2020-04-14 2023-07-04 Sony Interactive Entertainment Inc. Self-supervised AI-assisted sound effect recommendation for silent video
US11615312B2 (en) * 2020-04-14 2023-03-28 Sony Interactive Entertainment Inc. Self-supervised AI-assisted sound effect generation for silent video using multimodal clustering
KR102562731B1 (ko) * 2020-11-06 2023-08-01 연세대학교 산학협력단 자기 집중 모듈 및 이를 이용한 정규화 방법
EP4024878A1 (en) 2020-12-30 2022-07-06 Advanced Digital Broadcast S.A. A method and a system for testing audio-video synchronization of an audio-video player
CN112581980B (zh) * 2021-02-26 2021-05-25 中国科学院自动化研究所 时频通道注意力权重计算和向量化的方法和网络
CN114692085B (zh) * 2022-03-30 2024-07-16 北京字节跳动网络技术有限公司 特征提取方法、装置、存储介质及电子设备
US12020156B2 (en) * 2022-07-13 2024-06-25 Robert Bosch Gmbh Systems and methods for automatic alignment between audio recordings and labels extracted from a multitude of asynchronous sensors in urban settings

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9171578B2 (en) * 2010-08-06 2015-10-27 Futurewei Technologies, Inc. Video skimming methods and systems
US11422546B2 (en) * 2014-12-19 2022-08-23 Raytheon Technologies Corporation Multi-modal sensor data fusion for perception systems
US10109277B2 (en) * 2015-04-27 2018-10-23 Nuance Communications, Inc. Methods and apparatus for speech recognition using visual information
US20170178346A1 (en) * 2015-12-16 2017-06-22 High School Cube, Llc Neural network architecture for analyzing video data
GB2545661A (en) * 2015-12-21 2017-06-28 Nokia Technologies Oy A method for analysing media content
US20180018970A1 (en) * 2016-07-15 2018-01-18 Google Inc. Neural network for recognition of signals in multiple sensory domains
CN108108738B (zh) * 2017-11-28 2018-11-16 北京达佳互联信息技术有限公司 图像处理方法、装置及终端
US10964033B2 (en) * 2018-08-07 2021-03-30 Qualcomm Incorporated Decoupled motion models for object tracking

Also Published As

Publication number Publication date
CA3119042C (en) 2024-06-11
AU2019384731B2 (en) 2022-10-06
WO2020106737A1 (en) 2020-05-28
BR112021009617A2 (pt) 2021-08-31
US20200160889A1 (en) 2020-05-21
EP3884350A1 (en) 2021-09-29
CA3119042A1 (en) 2020-05-28
AU2019384731A1 (en) 2021-06-03

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