Fundamentals of speech recognition
Fundamentals of speech recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
Sound onset detection by applying psychoacoustic knowledge
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Analysis of the meter of acoustic musical signals
IEEE Transactions on Audio, Speech, and Language Processing
An experimental comparison of audio tempo induction algorithms
IEEE Transactions on Audio, Speech, and Language Processing
Audio chord labeling by musiological modeling and beat-synchronization
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Determination of nonprototypical valence and arousal in popular music: features and performances
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
Rhythm pattern representations for tempo detection in music
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Inferring personal traits from music listening history
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
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Rhythmic information plays an important role in Music Information Retrieval. Example applications include automatically annotating large databases by genre, meter, ballroom dance style or tempo, fully automated D.J.-ing, and audio segmentation for further retrieval tasks such as automatic chord labeling. In this article, we therefore provide an introductory overview over basic and current principles of tempo detection. Subsequently, we show how to improve on these by inclusion of ballroom dance style recognition. We introduce a feature set of 82 rhythmic features for rhythm analysis on real audio. With this set, data-driven identification of the meter and ballroom dance style, employing support vector machines, is carried out in a first step. Next, this information is used to more robustly detect tempo. We evaluate the suggested method on a large public database containing 1.8 k titles of standard and Latin ballroom dance music. Following extensive test runs, a clear boost in performance can be reported.