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Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification
We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical
Essentia: An Audio Analysis Library for Music Information Retrieval
Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013.
Multimodal Music Mood Classification Using Audio and Lyrics
It is demonstrated that lyrics and audio information are complementary, and can be combined to improve a classification system, and integrating this in a multimodal system allows an improvement in the overall performance.
A new approach to evaluating novel recommendations
A user-centric experiment shows that even though a social-based approach recommends less novel items than the authors' CB, users' perceived quality is better than those recommended by a pure CB method.
Music Mood Representations from Social Tags
This study demonstrates a particular relevancy of the basic emotions model with four mood clusters that can be sum-marized as: happy, sad, angry and tender.
A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals
The authors address the identification of predominant music instruments in polytimbral audio by previously dividing the original signal into several streams, and show that the performance was only enhanced if the recognition models are trained with the features extracted from the separated audio streams.
Automatic Classification of Drum Sounds: A Comparison of Feature Selection Methods and Classification Techniques
A comparative evaluation of automatic classification of a sound database containing more than six hundred drum sounds (kick, snare, hihat, toms and cymbals) using ten-fold cross-validation.
Roadmap for Music Information ReSearch
The use of Recurrence Quantification Analysis (RQA) features are explored for the scene classification task of the IEEE AASP Challenge for Detection and Classification of Acoustic Scenes and Events and improve accuracy when using a standard SVM classifier.