Fundamentals of speech recognition
Fundamentals of speech recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust speech recognition using the modulation spectrogram
Speech Communication - Special issue on robust speech recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval
MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval
Aggregate features and ADABOOST for music classification
Machine Learning
Joint acoustic and modulation frequency
EURASIP Journal on Applied Signal Processing
Modulation-scale analysis for content identification
IEEE Transactions on Signal Processing - Part II
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
Multigroup classification of audio signals using time-frequency parameters
IEEE Transactions on Multimedia
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Content-Based Information Fusion for Semi-Supervised Music Genre Classification
IEEE Transactions on Multimedia
Audio classification based on MPEG-7 spectral basis representations
IEEE Transactions on Circuits and Systems for Video Technology
On feature combination for music classification
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Unsupervised music genre classification with a model-based approach
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
An analysis of the GTZAN music genre dataset
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
An analysis of content-based classification of audio signals using a fuzzy c-means algorithm
Multimedia Tools and Applications
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In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation sub-bands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.