Matches in ScholarlyData for { ?s ?p ?o. }
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- 2 isPartOf proceedings.
- 2 keyword "Audio features".
- 2 keyword "Hybrid recommender system".
- 2 keyword "Music recommendation".
- 2 keyword "Tags".
- 2 keyword "Usage data".
- 2 title "Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail".
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- item-1 hasContent marcos-aurelio-domingues.
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- item-2 hasContent fabien-gouyon.
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- item-3 hasContent alipio-mario-jorge.
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- item-7 hasContent mohamed-sordo.
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- 4 creator dmitry-bogdanov.
- 4 creator mohamed-sordo.
- 4 creator perfecto-herrera.
- 4 creator yi-hsuan-yang.
- 4 type InProceedings.
- 4 label "Music Retagging Using Label Propagation and Robust Principal Component Analysis".
- 4 sameAs 4.
- 4 abstract "The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a preliminary study that aims at refining (retagging) social tags by exploiting the content similarity between tracks and the semantic redundancy of the track-tag matrix. The evaluated algorithms include a graph-based label propagation method that is often used in semi-supervised learning and a robust principal component analysis (PCA) algorithm that has led to state-of-the-art result in matrix completion. The result indicates that robust PCA with content similarity constraint is particularly effective; it improves the robustness of tagging against three types of synthetic errors and boosts the recall rate of music auto-tagging by 7% in a real-world setting.".
- 4 hasAuthorList authorList.
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- 4 keyword "music auto-tagging".
- 4 keyword "music retagging".
- 4 keyword "social tagging".
- 4 keyword "tag-based music retrieval".
- 4 title "Music Retagging Using Label Propagation and Robust Principal Component Analysis".
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- item-1 hasContent yi-hsuan-yang.
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- 5 creator alvaro-corral.
- 5 creator joan-serra.
- 5 creator martin-haro.
- 5 creator perfecto-herrera.
- 5 type InProceedings.
- 5 label "Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals".
- 5 sameAs 5.
- 5 abstract "Many sound-related applications use Mel-frequency cepstral coefficients (MFCC) to describe audio timbral content. Most of the research efforts dealing with MFCCs have been focused on the study of different classification and clustering algorithms, the use of complementary audio descriptors, or the effect of different distance measures. The goal of this paper is to focus on the statistical properties of the MFCC descriptor itself. For that purpose, we use a simple encoding process that maps a short-time MFCC vector to a dictionary of binary code-words. We study and characterize the rank-frequency distribution of such MFCC code-words, considering speech, music, and environmental sound sources. We show that, regardless of the sound source, MFCC code-words follow a shifted power-law distribution. This implies that there are a few code-words that occur very frequently and many that happen rarely, with no typical or characteristic code-word in the distribution. We also observe that the inner-structure of the most frequent code-words has characteristic patterns. For instance, we observe that close MFCC coefficients tend to have similar quantization values in the case of music signals. Finally, we study the rank-frequency distributions of individual music recordings and show that they present the same type of heavy-tailed distribution as found in the large-scale databases. This fact is exploited in two supervised semantic inference tasks: genre and instrument classification. In particular, we obtain similar classification results as the ones obtained by considering all frames in the recordings by just using 50 (properly selected) frames. Beyond this particular example, we believe that the fact that MFCC frames follow a power-law distribution could potentially have important implications for future applications dealing with audio signals.".
- 5 hasAuthorList authorList.
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- 5 keyword "MFCC".
- 5 keyword "large-scale data".
- 5 keyword "power-law".
- 5 keyword "sound retrieval".
- 5 keyword "timbre".
- 5 title "Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals".
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