WO2008110002A1 - Procédé et système pour l'évaluation automatique de fichiers numériques - Google Patents

Procédé et système pour l'évaluation automatique de fichiers numériques Download PDF

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Publication number
WO2008110002A1
WO2008110002A1 PCT/CA2008/000481 CA2008000481W WO2008110002A1 WO 2008110002 A1 WO2008110002 A1 WO 2008110002A1 CA 2008000481 W CA2008000481 W CA 2008000481W WO 2008110002 A1 WO2008110002 A1 WO 2008110002A1
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WO
WIPO (PCT)
Prior art keywords
database
files
reference files
learning model
training set
Prior art date
Application number
PCT/CA2008/000481
Other languages
English (en)
Inventor
Jocelyn Desbiens
Original Assignee
Webhitcontest 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
Priority claimed from CA2581466A external-priority patent/CA2581466C/fr
Priority claimed from US11/684,900 external-priority patent/US7873634B2/en
Application filed by Webhitcontest Inc. filed Critical Webhitcontest Inc.
Priority to EP08733585A priority Critical patent/EP2126727A4/fr
Publication of WO2008110002A1 publication Critical patent/WO2008110002A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
    • G06F16/68—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
    • G06F16/68—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/683—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]

Definitions

  • the present invention relates to a method and a system for automatic evaluation of digital files. More specifically, the present invention is concerned with a method for dynamic hit scoring.
  • Li et al. (US 2004/0231498) present a method for music classification comprising extracting features of a target file; extracting features of a training set; and classifying music signals.
  • Blum et al. (US 5,918,223) describe a method for classifying and ranking the similarity between individual audio files comprising supplying sets containing the features of classes of sound to a training algorithm yielding a set of vectors for each class of sound; submitting a target audio file to the same training algorithm to obtain a vector for the target file; and calculating the correlation distance between the vector for the target file and the vectors of each class, whereby the class which has the smallest distance to the target file is the class assigned to the target file.
  • Flannery et al. (US 6,545,209) present methods for classifying music according to similarity using a distance measure.
  • Gang et al. disclose a method for predicting musical preferences of a user, comprising the steps of building a first set of information relative to a catalog of musical selection; building a second set of information relative to the tastes of the user; and combining the information of the second set with the information of the first set to provide an expected rating for every song in the catalog.
  • a method for automatic evaluation of target files comprising the steps of building a database of reference files; for each target file, forming a training set comprising files from the database of reference files and building a test set from features of the target file; dynamically generating a learning model from the training set; and applying the learning model to the test set, whereby a value corresponding to the target file is predicted.
  • a method for automatic evaluation of songs comprising the step of building a database of hit songs; for each song to be evaluated, forming a training set comprising songs from the database of hit songs and building a test set from features of the song to be evaluated; dynamically generating a learning model from the training set; and applying the learning model to the test set; whereby a score corresponding to the song to be evaluated is predicted.
  • Figure is a flow chart of an embodiment of a method according to an aspect of the present invention.
  • Figure 2 illustrates a class separating hyperplane in a
  • An embodiment of the method according to an aspect of the present invention generally comprises an analysis step (step 100) and a dynamic scoring step (step 200).
  • a database of reference files is built.
  • the database of reference files comprises hit songs for example.
  • a number of files, such as MP3 files or other digital format, for example, of songs identified as hits are gathered, and numerical features that represent each one of them are extracted to form n-dimensional vectors of numerical features that represent each file, referred to as feature vectors, as well known in the art.
  • a number of features including for example timbre, rhythm, melody frequency etc, are extracted from the files to yield feature vectors corresponding to each one of them.
  • a hit score method a number of 84 features were extracted for example.
  • the feature vectors are stored in a database along with relevant information, such as for example, artist's name, genre etc (112).
  • Each MP3 file is rated, according to a predefined scheme, and also stored in a database (113).
  • the references files here exemplified as hit songs MP3, are selected according to a predefined scheme of rating. In the case of hit songs, scoring may originate from a number of sources, including for example, compilation of top 50 rankings, sales, air play etc.
  • the dynamic scoring step (step 200) generally comprises a learning phase and a predicting phase.
  • files from the reference database in regards to which the target file will be assessed are selected in a training set.
  • the training set is built by finding n closest feature vectors of the target file's feature vector in the database of feature vectors of the hits (116).
  • the distance/similarity between the target file's feature vector and each feature vector of the database of hits may be determined by using the Euclidian distance, the cosine distance or the Jensen-Shannon distribution similarity, as well known to people in the art.
  • PCA Principal Component Analysis
  • Singular Value Decomposition Singular Value Decomposition
  • non linear regression techniques known in the art such as (but not limited to): Neural Networks, Support Vector Machines, Generalized Additive Model, Classification and Regression Tree, Multivariate Adaptative Regression Splines, Hierarchical Mixture of Experts, Supervised Principal Component Analysis.
  • PCA is an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.
  • PCA can be used for dimensionality reduction in a data set while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher- order ones. Such low-order components often contain the "most important" aspects of the data. But this is not necessarily the case, depending on the application.
  • PCA is used to project multidimensional data to a lower dimensional space retaining as much as possible variability of the data. This technique is widely used in many areas of applied statistics. It is natural since interpretation and visualization in a fewer dimensional space is easier than in many dimensional space. Especially, dimensionality can be reduced to two or three, then plots and visual representation may be used to try and find some structure in the data. [0029] PCA is one of the techniques used for dimension reductions, as will now be briefly described.
  • M is an m-by-n matrix whose entries come from the field K, which is either the field of real numbers or the field of complex numbers. Then there exists a factorization of the form
  • M U ⁇ V ⁇ where U is an m-by-m unitary matrix over K, the matrix ⁇ is m-by-n with nonnegative numbers on the diagonal and zeros off the diagonal, and V * denotes the conjugate transpose of V, an n-by-n unitary matrix over K.
  • V * denotes the conjugate transpose of V, an n-by-n unitary matrix over K.
  • the matrix V thus contains a set of orthonormal "input" or
  • the matrix U contains a set of orthonormal "output" basis vector directions for M.
  • the matrix ⁇ contains the singular values, which can be thought of as scalar "gain controls" by which each corresponding input is multiplied to give a corresponding output.
  • i c J
  • i I ⁇ f )
  • the /c-th component can be found by subtracting the first k - 1 principal components from x:
  • PCA was described above as a technique, in Step 118, for reducing dimensionality of the learning set feature space, the learning set comprising nearest neighbors from the target file.
  • a learning model is dynamically generated (130), using a well-known theoretical algorithm called Support Vector Model (SVM) for example, as will now be described, using a software MCubixTM developed by Diagnos Inc. for example.
  • SVM Support Vector Model
  • a set of training examples S/ ⁇ (Xi,yi),(x2,y2),---,(x ⁇ ,y ⁇ ) ⁇ of size / from a fixed but unknown distribution p(x,y) describing the learning task is given.
  • the term-frequency vectors x, represent documents and y, ⁇ 1 indicates whether a document has been labeled with the positive class or not.
  • the SVM aims to find a decision rule h c: x ⁇ ⁇ -1 ,+1 ⁇ that classifies the documents as accurately as possible based on the training set S / .
  • the constraints require that all training examples are classified correctly, allowing for some outliers symbolized by the slack variables ⁇ , . If a training example lies on the wrong side of the hyperplane, the corresponding ⁇ , is greater than 0.
  • the factor C is a parameter that allows for trading off training error against model complexity. In the limit C ⁇ °° no training error is allowed. This setting is called hard margin SVM.
  • a classifier with finite C is also called a soft margin Support Vector Machine.
  • All training examples with ⁇ , > 0 at the solution are called support vectors.
  • the Support vectors are situated right at the margin (see the solid circle and squares in Figure 2) and define the hyperplane.
  • the definition of a hyperplane by the support vectors is especially advantageous in high dimensional feature spaces because a comparatively small number of parameters — the ⁇ in the sum of equation — is required.
  • SVM have been introduced within the context of statistical learning theory and structural risk minimization. In the methods one solves convex optimization problems, typically quadratic programs.
  • Least Squares Support Vector Machines are reformulations to standard SVM. LS- SVM are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations. Links between kernel versions of classical pattern recognition algorithms such as kernel Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control also exist.
  • two hyper-parameters are needed, including a regularization parameter ⁇ , determining the trade-off between the fitting error minimization and smoothness, and the bandwidth ⁇ 2 , at least in the common case of the RBF kernel.
  • These two hyper-parameters are automatically computed by doing a grid search over the parameter space and picking the minimum. This procedure iteratively zooms to the candidate optimum.
  • the learning model generated in step 130 is applied to the test set, so as to determine a value corresponding to the target song (150).
  • the rating of the target file is based on the test set and the learning set, the target file being assessed relative to the training set.
  • a storing phase may further comprise storing the predicted values in a result database.
  • the learning model is discarded after prediction for the target file (160), before the method is applied to another file to be evaluated (170).
  • the training set is rebuilt by updating the closest neighbours and hyper- parameters are automatically updated, resulting in a dynamic scoring method.
  • the present method allows an automatic learning on a dynamic neighbourhood.
  • the method may be used for pre-selecting songs in the contest of a hit contest for example, typically based on the popularity of the songs.
  • the present adaptative method may be applied to evaluate a range of type of files, i.e. compression format, nature of files etc... with an increased accuracy in highly non-linear fields, by providing a dynamic learning phase.

Abstract

La présente invention se rapporte à un procédé pour l'évaluation automatique de fichiers cible, comprenant les étapes de création d'une base de données de fichiers de référence; pour chaque fichier cible, la formation d'un ensemble d'apprentissage contenant des fichiers extraits de la base de données de fichiers de référence et la création d'un ensemble de test à partir de caractéristiques du fichier cible; la génération dynamique d'un modèle d'apprentissage à partir de l'ensemble d'apprentissage; et l'application du modèle d'apprentissage à l'ensemble de test, ce qui permet de prédire une valeur correspondant au fichier cible.
PCT/CA2008/000481 2007-03-12 2008-03-12 Procédé et système pour l'évaluation automatique de fichiers numériques WO2008110002A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP08733585A EP2126727A4 (fr) 2007-03-12 2008-03-12 Procédé et système pour l'évaluation automatique de fichiers numériques

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CA2,581,466 2007-03-12
US11/684,900 2007-03-12
CA2581466A CA2581466C (fr) 2007-03-12 2007-03-12 Methode et systeme d'evaluation automatique des fichiers numeriques
US11/684,900 US7873634B2 (en) 2007-03-12 2007-03-12 Method and a system for automatic evaluation of digital files

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Cited By (18)

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US8121830B2 (en) 2008-10-24 2012-02-21 The Nielsen Company (Us), Llc Methods and apparatus to extract data encoded in media content
US8359205B2 (en) 2008-10-24 2013-01-22 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8508357B2 (en) 2008-11-26 2013-08-13 The Nielsen Company (Us), Llc Methods and apparatus to encode and decode audio for shopper location and advertisement presentation tracking
US8666528B2 (en) 2009-05-01 2014-03-04 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
CN104254887A (zh) * 2012-09-24 2014-12-31 希特兰布公司 用于评估卡拉ok用户的方法和系统
US8959016B2 (en) 2002-09-27 2015-02-17 The Nielsen Company (Us), Llc Activating functions in processing devices using start codes embedded in audio
US9100132B2 (en) 2002-07-26 2015-08-04 The Nielsen Company (Us), Llc Systems and methods for gathering audience measurement data
US9197421B2 (en) 2012-05-15 2015-11-24 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9210208B2 (en) 2011-06-21 2015-12-08 The Nielsen Company (Us), Llc Monitoring streaming media content
US9313544B2 (en) 2013-02-14 2016-04-12 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9336784B2 (en) 2013-07-31 2016-05-10 The Nielsen Company (Us), Llc Apparatus, system and method for merging code layers for audio encoding and decoding and error correction thereof
US9380356B2 (en) 2011-04-12 2016-06-28 The Nielsen Company (Us), Llc Methods and apparatus to generate a tag for media content
US9609034B2 (en) 2002-12-27 2017-03-28 The Nielsen Company (Us), Llc Methods and apparatus for transcoding metadata
US9667365B2 (en) 2008-10-24 2017-05-30 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US9711153B2 (en) 2002-09-27 2017-07-18 The Nielsen Company (Us), Llc Activating functions in processing devices using encoded audio and detecting audio signatures
US9711152B2 (en) 2013-07-31 2017-07-18 The Nielsen Company (Us), Llc Systems apparatus and methods for encoding/decoding persistent universal media codes to encoded audio
US9762965B2 (en) 2015-05-29 2017-09-12 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
CN112989606A (zh) * 2021-03-16 2021-06-18 上海哥瑞利软件股份有限公司 数据算法模型检验方法、系统及计算机存储介质

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US9100132B2 (en) 2002-07-26 2015-08-04 The Nielsen Company (Us), Llc Systems and methods for gathering audience measurement data
US9711153B2 (en) 2002-09-27 2017-07-18 The Nielsen Company (Us), Llc Activating functions in processing devices using encoded audio and detecting audio signatures
US8959016B2 (en) 2002-09-27 2015-02-17 The Nielsen Company (Us), Llc Activating functions in processing devices using start codes embedded in audio
US9900652B2 (en) 2002-12-27 2018-02-20 The Nielsen Company (Us), Llc Methods and apparatus for transcoding metadata
US9609034B2 (en) 2002-12-27 2017-03-28 The Nielsen Company (Us), Llc Methods and apparatus for transcoding metadata
US8554545B2 (en) 2008-10-24 2013-10-08 The Nielsen Company (Us), Llc Methods and apparatus to extract data encoded in media content
US11256740B2 (en) 2008-10-24 2022-02-22 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8359205B2 (en) 2008-10-24 2013-01-22 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US11809489B2 (en) 2008-10-24 2023-11-07 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8121830B2 (en) 2008-10-24 2012-02-21 The Nielsen Company (Us), Llc Methods and apparatus to extract data encoded in media content
US10467286B2 (en) 2008-10-24 2019-11-05 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US11386908B2 (en) 2008-10-24 2022-07-12 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US9667365B2 (en) 2008-10-24 2017-05-30 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US10134408B2 (en) 2008-10-24 2018-11-20 The Nielsen Company (Us), Llc Methods and apparatus to perform audio watermarking and watermark detection and extraction
US8508357B2 (en) 2008-11-26 2013-08-13 The Nielsen Company (Us), Llc Methods and apparatus to encode and decode audio for shopper location and advertisement presentation tracking
US11948588B2 (en) 2009-05-01 2024-04-02 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
US10003846B2 (en) 2009-05-01 2018-06-19 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
US11004456B2 (en) 2009-05-01 2021-05-11 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
US10555048B2 (en) 2009-05-01 2020-02-04 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
US8666528B2 (en) 2009-05-01 2014-03-04 The Nielsen Company (Us), Llc Methods, apparatus and articles of manufacture to provide secondary content in association with primary broadcast media content
US9380356B2 (en) 2011-04-12 2016-06-28 The Nielsen Company (Us), Llc Methods and apparatus to generate a tag for media content
US9681204B2 (en) 2011-04-12 2017-06-13 The Nielsen Company (Us), Llc Methods and apparatus to validate a tag for media
US11252062B2 (en) 2011-06-21 2022-02-15 The Nielsen Company (Us), Llc Monitoring streaming media content
US9838281B2 (en) 2011-06-21 2017-12-05 The Nielsen Company (Us), Llc Monitoring streaming media content
US9515904B2 (en) 2011-06-21 2016-12-06 The Nielsen Company (Us), Llc Monitoring streaming media content
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US9197421B2 (en) 2012-05-15 2015-11-24 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
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US9357261B2 (en) 2013-02-14 2016-05-31 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9313544B2 (en) 2013-02-14 2016-04-12 The Nielsen Company (Us), Llc Methods and apparatus to measure exposure to streaming media
US9711152B2 (en) 2013-07-31 2017-07-18 The Nielsen Company (Us), Llc Systems apparatus and methods for encoding/decoding persistent universal media codes to encoded audio
US9336784B2 (en) 2013-07-31 2016-05-10 The Nielsen Company (Us), Llc Apparatus, system and method for merging code layers for audio encoding and decoding and error correction thereof
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