Fundamentals of speech recognition
Fundamentals of speech recognition
Machine Learning
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
International Journal of Computer Vision
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Modeling timbre distance with temporal statistics from polyphonic music
IEEE Transactions on Audio, Speech, and Language Processing
Temporal Feature Integration for Music Genre Classification
IEEE Transactions on Audio, Speech, and Language Processing
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Audio classification based on MPEG-7 spectral basis representations
IEEE Transactions on Circuits and Systems for Video Technology
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A central problem in music information retrieval is audio-based music classification. Current music classification systems follow a frame-based analysis model. A whole song is split into frames, where a feature vector is extracted from each local frame. Each song can then be represented by a set of feature vectors. How to utilize the feature set for global song-level classification is an important problem in music classification. Previous studies have used summary features and probability models which are either overly restrictive in modeling power or numerically too difficult to solve. In this paper, we investigate the bag-of-features approach for music classification which can effectively aggregate the local features for song-level feature representation. Moreover, we have extended the standard bag-of-features approach by proposing a multiple codebook model to exploit the randomness in the generation of codebooks. Experimental results for genre classification and artist identification on benchmark data sets show that the proposed classification system is highly competitive against the standard methods.