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Outline
           Problem Formulation
                    Motivation
              Proposed Method
           Experimental Results
                   Future Work




  Music Genre Classification Using Explicit
Semantic Analysis and Sparsity-Eager Support
              Vector Machines

                     Kamelia Aryafar

                    Drexel University
               Computer Science Department


                   February 18, 2012


               Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                  Problem Formulation
                           Motivation
                     Proposed Method
                  Experimental Results
                          Future Work



1   Problem Formulation
2   Motivation
      Challenges
      Related Work
3   Proposed Method
      Feature Selection
      Fractional TF-IDF
      Sparsity-Eager SVM Genre Classification
4   Experimental Results
      Benchmark Data set
      Results
5   Future Work

                      Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                 Problem Formulation
                          Motivation    Challenges
                    Proposed Method     Related Work
                 Experimental Results
                         Future Work


Motivation




     Many systems are exposed to high-dimensional data, e.g.
     images, image sequences and even scalar signals.
     The high dimensional data could be also multimodal.

                     Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
               Problem Formulation
                        Motivation    Challenges
                  Proposed Method     Related Work
               Experimental Results
                       Future Work


Motivation



                      (Multimodal Mixture)




             (Source I)                       (Source II)



                   Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation           Challenges
                      Proposed Method            Related Work
                   Experimental Results
                           Future Work


BSS Illustration
  Artificial gaussian mixture of two audio sources:




                              (Violin mixture)




                                          (I)


                                          (II)

                       Kamelia Aryafar           Music Genre Classification Using Explicit Semantic Analysis
Outline
                     Problem Formulation
                              Motivation    Challenges
                        Proposed Method     Related Work
                     Experimental Results
                             Future Work


Motivation

  The problem of genre classification:




             (Violin playing)




                         Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                     Problem Formulation
                              Motivation    Challenges
                        Proposed Method     Related Work
                     Experimental Results
                             Future Work


Motivation

  The problem of genre classification:




             (Violin playing)




                  Genre Label: Classic Music/Violin


                         Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation    Challenges
                      Proposed Method     Related Work
                   Experimental Results
                           Future Work


Music Genre Classification

  Goal
  Music genre classification is the problem of categorization of a
  piece of music into its corresponding categorical labels. The
  goal of automatic music genre classification is to estimate
  genre labels for test audio sequences in large data sets.




                       Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation    Challenges
                      Proposed Method     Related Work
                   Experimental Results
                           Future Work


Music Genre Classification

  Goal
  Music genre classification is the problem of categorization of a
  piece of music into its corresponding categorical labels. The
  goal of automatic music genre classification is to estimate
  genre labels for test audio sequences in large data sets.

  Motivation
      Exponential growth in available music data sets
      Cost reduction
      Extension to similar tasks

                       Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
             Problem Formulation
                      Motivation    Challenges
                Proposed Method     Related Work
             Experimental Results
                     Future Work


Challenges




                 Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                  Problem Formulation
                           Motivation    Challenges
                     Proposed Method     Related Work
                  Experimental Results
                          Future Work


Challenges




     The robust representation of audio signals in terms of
     low-level features or high-level audio keywords
     The construction of an automatic learning schema to
     classify these representative features into music genres.
                      Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
             Problem Formulation
                      Motivation    Challenges
                Proposed Method     Related Work
             Experimental Results
                     Future Work


Proposed Method




                  Kamelia Aryafar   Music Genre Classification Using Explicit Semantic Analysis
Outline
                 Problem Formulation
                          Motivation    Challenges
                    Proposed Method     Related Work
                 Experimental Results
                         Future Work


Proposed Method




    Abstract layer to represent features in terms of concepts
    Invariant to feature selection

                     Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                  Problem Formulation
                           Motivation    Challenges
                     Proposed Method     Related Work
                  Experimental Results
                          Future Work


TF-IDF Representation


  Goal
  Create a high-level abstraction of low-level audio features
  (codewords of MFCCs) to enhance music genre classification.




                      Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation    Challenges
                      Proposed Method     Related Work
                   Experimental Results
                           Future Work


TF-IDF Representation


  Goal
  Create a high-level abstraction of low-level audio features
  (codewords of MFCCs) to enhance music genre classification.

  ESA Model
  Explicit semantic analysis (ESA) utilizes term-frequency (tf) and
  inverse document frequency (idf) weighting schemata to
  represent low-level textual information in terms of concepts in
  higher-dimensional space.



                       Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
              Problem Formulation
                       Motivation    Challenges
                 Proposed Method     Related Work
              Experimental Results
                      Future Work


TF-IDF Representation



                                          EC,D [i, j] = tfidf (Ci , δj ).




                  Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation    Challenges
                      Proposed Method     Related Work
                   Experimental Results
                           Future Work


TF-IDF Representation



                                               EC,D [i, j] = tfidf (Ci , δj ).




  TF-IDF
  The relationship between a codeword and a concept
  (document) pair will be captured through the so-called tf-idf
  value of the word-concept pair.

                       Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                 Problem Formulation
                                        Feature Selection
                          Motivation
                                        Fractional TF-IDF
                    Proposed Method
                                        Sparsity-Eager SVM Genre Classification
                 Experimental Results
                         Future Work


Mel Frequency Cepstral Coefficients

  MFCCs represent short-term power spectrum of sound and are
  known to be effective for music classification systems.




                     Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                 Problem Formulation
                                        Feature Selection
                          Motivation
                                        Fractional TF-IDF
                    Proposed Method
                                        Sparsity-Eager SVM Genre Classification
                 Experimental Results
                         Future Work


Mel Frequency Cepstral Coefficients

  MFCCs represent short-term power spectrum of sound and are
  known to be effective for music classification systems.


                            Pre-processing
                            For a large data set, k-means clustering
                            of MFCCs creates the audio code-book,
                            D = {δ1 , ..., δk }, using the cosine
                            similarity distance measure to reduce the
                            complexity of the feature space.



                     Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
               Problem Formulation
                                      Feature Selection
                        Motivation
                                      Fractional TF-IDF
                  Proposed Method
                                      Sparsity-Eager SVM Genre Classification
               Experimental Results
                       Future Work


Fractional TF-IDF [2]




                   Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
               Problem Formulation
                                      Feature Selection
                        Motivation
                                      Fractional TF-IDF
                  Proposed Method
                                      Sparsity-Eager SVM Genre Classification
               Experimental Results
                       Future Work


Fractional TF-IDF [2]




                 tfidf (C, δ) = tf (C, δ) × idfδ
                    EC,D [i, j] = tfidf (Ci , δj )
                   Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
              Problem Formulation
                                     Feature Selection
                       Motivation
                                     Fractional TF-IDF
                 Proposed Method
                                     Sparsity-Eager SVM Genre Classification
              Experimental Results
                      Future Work


Concept-based Representation of Audio Features




                  Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                                           Feature Selection
                             Motivation
                                           Fractional TF-IDF
                       Proposed Method
                                           Sparsity-Eager SVM Genre Classification
                    Experimental Results
                            Future Work


Training the Classifier


  ESA representation of the training set
  The set E(T ) of (ESA-vector, label) pairs will be provided as the
  training data to a supervised classifier algorithm.




                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                                           Feature Selection
                             Motivation
                                           Fractional TF-IDF
                       Proposed Method
                                           Sparsity-Eager SVM Genre Classification
                    Experimental Results
                            Future Work


Training the Classifier


  ESA representation of the training set
  The set E(T ) of (ESA-vector, label) pairs will be provided as the
  training data to a supervised classifier algorithm.

  Outcome
  The set of hyperplanes that define the gaps between genres,
  are the outcome of the training on E(T ).




                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                                          Feature Selection
                            Motivation
                                          Fractional TF-IDF
                      Proposed Method
                                          Sparsity-Eager SVM Genre Classification
                   Experimental Results
                           Future Work


Genre Classification


  Classifier selection
  Sparsity-Eager support vector machine ( 1 -SVM) is used to
  assign samples to their genre categories.




                       Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                                           Feature Selection
                             Motivation
                                           Fractional TF-IDF
                       Proposed Method
                                           Sparsity-Eager SVM Genre Classification
                    Experimental Results
                            Future Work


Genre Classification


  Classifier selection
  Sparsity-Eager support vector machine ( 1 -SVM) is used to
  assign samples to their genre categories.

   1 -SVM
  In contrast to the the original 2 -SVM, only a small subset of the
  training examples contribute to the formation of the final
  classifier.



                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                                           Feature Selection
                             Motivation
                                           Fractional TF-IDF
                       Proposed Method
                                           Sparsity-Eager SVM Genre Classification
                    Experimental Results
                            Future Work


Sparsity-Eager SVM[1]

  Classification
  Given a set of M training examples, we aim to find a sample
  subset such that (i) subset is sufficiently sparse, and (ii) the
  classifier has a sufficiently low empirical loss and therefore
  sufficiently large separating margin.




                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                                           Feature Selection
                             Motivation
                                           Fractional TF-IDF
                       Proposed Method
                                           Sparsity-Eager SVM Genre Classification
                    Experimental Results
                            Future Work


Sparsity-Eager SVM[1]

  Classification
  Given a set of M training examples, we aim to find a sample
  subset such that (i) subset is sufficiently sparse, and (ii) the
  classifier has a sufficiently low empirical loss and therefore
  sufficiently large separating margin.

  Why   1 -SVM
  (i) obtaining higher generalization accuracy on new (test)
  examples, (ii) increasing the robustness against overfitting to
  the training examples, and (iii) providing scalability in terms of
  the classification complexity.

                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                 Problem Formulation
                          Motivation    Benchmark Data set
                    Proposed Method     Results
                 Experimental Results
                         Future Work


Data set Description

   Data set:                                 Genre                 Samples
   We use the publicly                     alternative               145
   available benchmark                        blues                  120
   dataset for audio                       electronic                113
   classification and                      folk-country               222
   clustering proposed by               funk soul/R&B                47
   Homburg et al [3]. The                      jazz                  319
   dataset contains                            pop                   116
   samples of 1886 songs                  rap/hip-hop                300
   obtained from the                           rock                  504
   Garageband site.

                     Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                  Problem Formulation
                           Motivation    Benchmark Data set
                     Proposed Method     Results
                  Experimental Results
                          Future Work


Experimental Setup


  Parameters setup
  Validation method: 10-fold cross validation
  Performance measure: classification accuracy rate
  Similarity measure: cosine distance




                      Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                  Problem Formulation
                           Motivation    Benchmark Data set
                     Proposed Method     Results
                  Experimental Results
                          Future Work


Experimental Setup


  Parameters setup
  Validation method: 10-fold cross validation
  Performance measure: classification accuracy rate
  Similarity measure: cosine distance

  Comparative features
  Aggregation of MFCC features (AM)
  Temporal, spectral and phase (TSPS)



                      Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                   Problem Formulation
                            Motivation     Benchmark Data set
                      Proposed Method      Results
                   Experimental Results
                           Future Work


Genre Classification Accuracy Results

                                                       ESA
       Classifier       AM          TSPS
                                               k = 1000 k            = 5000
        Random       22.39         21.68         29.51               25.40
         k-NN        35.83         47.40         48.59               51.88
         SVM         40.81         51.81         53.76               57.81


  Comparison
  Aggregation of MFCC features (AM) and temporal, spectral and
  phase (TSPS) features are compared to the ESA
  representation of MFCC features.

                       Kamelia Aryafar     Music Genre Classification Using Explicit Semantic Analysis
Outline
                                                                      Problem Formulation
                                                                               Motivation                      Benchmark Data set
                                                                         Proposed Method                       Results
                                                                      Experimental Results
                                                                              Future Work


True Positive Accuracy Rate

                                                    50
                                                                                                                                                          l1−SVM
                                                                                                                                                          log−regression
                                                    45
                                                                                                                                                          l2−SVM
                                                                                                                                                          l1−regression
                                                    40
       classification accuracy rate (%) per genre




                                                    35


                                                    30


                                                    25


                                                    20


                                                    15


                                                    10


                                                     5


                                                     0
                                                             1            2           3                4              5        6            7            8
                                                            Alternative    Blues   Electronic   Folk−Country        Jazz     Pop     Rock       Rap/Hip−hop




                                                         Figure: True positive genre classification rate

                                                                          Kamelia Aryafar                      Music Genre Classification Using Explicit Semantic Analysis
Outline
              Problem Formulation
                       Motivation    Benchmark Data set
                 Proposed Method     Results
              Experimental Results
                      Future Work


Classifier Convergence Time




            Figure: Classifier convergence time

                  Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                Problem Formulation
                         Motivation    Benchmark Data set
                   Proposed Method     Results
                Experimental Results
                        Future Work


Classification Accuracy vs. Training Samples




           Figure: Accuracy rate for different samples

                    Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                      Problem Formulation
                               Motivation
                         Proposed Method
                      Experimental Results
                              Future Work


Future Work

                   MFCC Representation


                                                                         CCA Space


   Audio Signals                               ESA-Encoding
    (concepts)
                            ...




                                                  CCA




    Lyrics Data                                  TF-IDF
                     TF Representation
    (concepts)                                 Representation




                          Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                    Problem Formulation
                             Motivation
                       Proposed Method
                    Experimental Results
                            Future Work


Future Work...

                  MFCC Representation


                                                              CCA Space


    Audio Query                               ESAENCODING
                           ...




                       Paired Textual
                        Data (Lyrics)




                        Kamelia Aryafar    Music Genre Classification Using Explicit Semantic Analysis
Outline
                                 Problem Formulation
                                          Motivation
                                    Proposed Method
                                 Experimental Results
                                         Future Work


Questions?

  Thank you!
  [1] Kamelia Aryafar, sina Jafarpour, and Ali Shokoufandeh.
      Automatic musical genre classification using sparsity-eager support vector machines.
      In NIME’12, 2012.
  [2] Kamelia Aryafar and Ali Shokoufandeh.
      Music genre classification using explicit semantic analysis.
      In Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and
      multimodal strategies, MIRUM ’11, pages 33–38, New York, NY, USA, 2011. ACM.
                                             ¨
  [3] Helge Homburg, Ingo Mierswa, Bulent Moller, Katharina Morik, and Michael Wurst.
                                      ¨
      A benchmark dataset for audio classification and clustering.
      In ISMIR, pages 528–531, 2005.

  Acknowledments

  This work was funded in part by Office of Naval Research (ONR) grant N00014-04-1-0363 and United States
  National Science Foundation grant N0803670.




                                      Kamelia Aryafar        Music Genre Classification Using Explicit Semantic Analysis

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Kamelia Aryafar: Musical Genre Classification Using Sparsity-Eager Support Vector Machines and Extended Semantic Analysis

  • 1. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work Music Genre Classification Using Explicit Semantic Analysis and Sparsity-Eager Support Vector Machines Kamelia Aryafar Drexel University Computer Science Department February 18, 2012 Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 2. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work 1 Problem Formulation 2 Motivation Challenges Related Work 3 Proposed Method Feature Selection Fractional TF-IDF Sparsity-Eager SVM Genre Classification 4 Experimental Results Benchmark Data set Results 5 Future Work Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 3. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Motivation Many systems are exposed to high-dimensional data, e.g. images, image sequences and even scalar signals. The high dimensional data could be also multimodal. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 4. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Motivation (Multimodal Mixture) (Source I) (Source II) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 5. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work BSS Illustration Artificial gaussian mixture of two audio sources: (Violin mixture) (I) (II) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 6. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Motivation The problem of genre classification: (Violin playing) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 7. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Motivation The problem of genre classification: (Violin playing) Genre Label: Classic Music/Violin Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 8. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Music Genre Classification Goal Music genre classification is the problem of categorization of a piece of music into its corresponding categorical labels. The goal of automatic music genre classification is to estimate genre labels for test audio sequences in large data sets. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 9. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Music Genre Classification Goal Music genre classification is the problem of categorization of a piece of music into its corresponding categorical labels. The goal of automatic music genre classification is to estimate genre labels for test audio sequences in large data sets. Motivation Exponential growth in available music data sets Cost reduction Extension to similar tasks Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 10. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Challenges Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 11. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Challenges The robust representation of audio signals in terms of low-level features or high-level audio keywords The construction of an automatic learning schema to classify these representative features into music genres. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 12. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Proposed Method Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 13. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work Proposed Method Abstract layer to represent features in terms of concepts Invariant to feature selection Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 14. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work TF-IDF Representation Goal Create a high-level abstraction of low-level audio features (codewords of MFCCs) to enhance music genre classification. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 15. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work TF-IDF Representation Goal Create a high-level abstraction of low-level audio features (codewords of MFCCs) to enhance music genre classification. ESA Model Explicit semantic analysis (ESA) utilizes term-frequency (tf) and inverse document frequency (idf) weighting schemata to represent low-level textual information in terms of concepts in higher-dimensional space. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 16. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work TF-IDF Representation EC,D [i, j] = tfidf (Ci , δj ). Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 17. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future Work TF-IDF Representation EC,D [i, j] = tfidf (Ci , δj ). TF-IDF The relationship between a codeword and a concept (document) pair will be captured through the so-called tf-idf value of the word-concept pair. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 18. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Mel Frequency Cepstral Coefficients MFCCs represent short-term power spectrum of sound and are known to be effective for music classification systems. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 19. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Mel Frequency Cepstral Coefficients MFCCs represent short-term power spectrum of sound and are known to be effective for music classification systems. Pre-processing For a large data set, k-means clustering of MFCCs creates the audio code-book, D = {δ1 , ..., δk }, using the cosine similarity distance measure to reduce the complexity of the feature space. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 20. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Fractional TF-IDF [2] Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 21. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Fractional TF-IDF [2] tfidf (C, δ) = tf (C, δ) × idfδ EC,D [i, j] = tfidf (Ci , δj ) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 22. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Concept-based Representation of Audio Features Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 23. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Training the Classifier ESA representation of the training set The set E(T ) of (ESA-vector, label) pairs will be provided as the training data to a supervised classifier algorithm. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 24. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Training the Classifier ESA representation of the training set The set E(T ) of (ESA-vector, label) pairs will be provided as the training data to a supervised classifier algorithm. Outcome The set of hyperplanes that define the gaps between genres, are the outcome of the training on E(T ). Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 25. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Genre Classification Classifier selection Sparsity-Eager support vector machine ( 1 -SVM) is used to assign samples to their genre categories. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 26. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Genre Classification Classifier selection Sparsity-Eager support vector machine ( 1 -SVM) is used to assign samples to their genre categories. 1 -SVM In contrast to the the original 2 -SVM, only a small subset of the training examples contribute to the formation of the final classifier. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 27. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Sparsity-Eager SVM[1] Classification Given a set of M training examples, we aim to find a sample subset such that (i) subset is sufficiently sparse, and (ii) the classifier has a sufficiently low empirical loss and therefore sufficiently large separating margin. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 28. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future Work Sparsity-Eager SVM[1] Classification Given a set of M training examples, we aim to find a sample subset such that (i) subset is sufficiently sparse, and (ii) the classifier has a sufficiently low empirical loss and therefore sufficiently large separating margin. Why 1 -SVM (i) obtaining higher generalization accuracy on new (test) examples, (ii) increasing the robustness against overfitting to the training examples, and (iii) providing scalability in terms of the classification complexity. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 29. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Data set Description Data set: Genre Samples We use the publicly alternative 145 available benchmark blues 120 dataset for audio electronic 113 classification and folk-country 222 clustering proposed by funk soul/R&B 47 Homburg et al [3]. The jazz 319 dataset contains pop 116 samples of 1886 songs rap/hip-hop 300 obtained from the rock 504 Garageband site. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 30. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Experimental Setup Parameters setup Validation method: 10-fold cross validation Performance measure: classification accuracy rate Similarity measure: cosine distance Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 31. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Experimental Setup Parameters setup Validation method: 10-fold cross validation Performance measure: classification accuracy rate Similarity measure: cosine distance Comparative features Aggregation of MFCC features (AM) Temporal, spectral and phase (TSPS) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 32. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Genre Classification Accuracy Results ESA Classifier AM TSPS k = 1000 k = 5000 Random 22.39 21.68 29.51 25.40 k-NN 35.83 47.40 48.59 51.88 SVM 40.81 51.81 53.76 57.81 Comparison Aggregation of MFCC features (AM) and temporal, spectral and phase (TSPS) features are compared to the ESA representation of MFCC features. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 33. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work True Positive Accuracy Rate 50 l1−SVM log−regression 45 l2−SVM l1−regression 40 classification accuracy rate (%) per genre 35 30 25 20 15 10 5 0 1 2 3 4 5 6 7 8 Alternative Blues Electronic Folk−Country Jazz Pop Rock Rap/Hip−hop Figure: True positive genre classification rate Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 34. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Classifier Convergence Time Figure: Classifier convergence time Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 35. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future Work Classification Accuracy vs. Training Samples Figure: Accuracy rate for different samples Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 36. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work Future Work MFCC Representation CCA Space Audio Signals ESA-Encoding (concepts) ... CCA Lyrics Data TF-IDF TF Representation (concepts) Representation Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 37. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work Future Work... MFCC Representation CCA Space Audio Query ESAENCODING ... Paired Textual Data (Lyrics) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  • 38. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work Questions? Thank you! [1] Kamelia Aryafar, sina Jafarpour, and Ali Shokoufandeh. Automatic musical genre classification using sparsity-eager support vector machines. In NIME’12, 2012. [2] Kamelia Aryafar and Ali Shokoufandeh. Music genre classification using explicit semantic analysis. In Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies, MIRUM ’11, pages 33–38, New York, NY, USA, 2011. ACM. ¨ [3] Helge Homburg, Ingo Mierswa, Bulent Moller, Katharina Morik, and Michael Wurst. ¨ A benchmark dataset for audio classification and clustering. In ISMIR, pages 528–531, 2005. Acknowledments This work was funded in part by Office of Naval Research (ONR) grant N00014-04-1-0363 and United States National Science Foundation grant N0803670. Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis