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

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

  1. 1. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work Music Genre Classification Using ExplicitSemantic 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. 2. Outline Problem Formulation Motivation Proposed Method Experimental Results Future Work1 Problem Formulation2 Motivation Challenges Related Work3 Proposed Method Feature Selection Fractional TF-IDF Sparsity-Eager SVM Genre Classification4 Experimental Results Benchmark Data set Results5 Future Work Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  3. 3. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMotivation 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. 4. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMotivation (Multimodal Mixture) (Source I) (Source II) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  5. 5. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkBSS Illustration Artificial gaussian mixture of two audio sources: (Violin mixture) (I) (II) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  6. 6. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMotivation The problem of genre classification: (Violin playing) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  7. 7. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMotivation The problem of genre classification: (Violin playing) Genre Label: Classic Music/Violin Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  8. 8. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMusic 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. 9. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkMusic 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. 10. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkChallenges Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  11. 11. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkChallenges 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. 12. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkProposed Method Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  13. 13. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkProposed Method Abstract layer to represent features in terms of concepts Invariant to feature selection Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  14. 14. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkTF-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. 15. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkTF-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. 16. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkTF-IDF Representation EC,D [i, j] = tfidf (Ci , δj ). Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  17. 17. Outline Problem Formulation Motivation Challenges Proposed Method Related Work Experimental Results Future WorkTF-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. 18. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkMel 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. 19. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkMel 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. 20. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkFractional TF-IDF [2] Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  21. 21. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkFractional 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. 22. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkConcept-based Representation of Audio Features Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  23. 23. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkTraining 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. 24. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkTraining 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. 25. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkGenre 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. 26. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkGenre 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. 27. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkSparsity-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. 28. Outline Problem Formulation Feature Selection Motivation Fractional TF-IDF Proposed Method Sparsity-Eager SVM Genre Classification Experimental Results Future WorkSparsity-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. 29. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkData 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. 30. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkExperimental 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. 31. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkExperimental 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. 32. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkGenre 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. 33. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkTrue 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. 34. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkClassifier Convergence Time Figure: Classifier convergence time Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  35. 35. Outline Problem Formulation Motivation Benchmark Data set Proposed Method Results Experimental Results Future WorkClassification Accuracy vs. Training Samples Figure: Accuracy rate for different samples Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  36. 36. Outline Problem Formulation Motivation Proposed Method Experimental Results Future WorkFuture 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. 37. Outline Problem Formulation Motivation Proposed Method Experimental Results Future WorkFuture Work... MFCC Representation CCA Space Audio Query ESAENCODING ... Paired Textual Data (Lyrics) Kamelia Aryafar Music Genre Classification Using Explicit Semantic Analysis
  38. 38. Outline Problem Formulation Motivation Proposed Method Experimental Results Future WorkQuestions? 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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