International Journal of Computer Vision
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Kernel independent component analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Towards efficient automated singer identification in large music databases
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Towards musical query-by-semantic-description using the CAL500 data set
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Consumer Search and Retailer Strategies in the Presence of Online Music Sharing
Journal of Management Information Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Combining audio content and social context for semantic music discovery
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
CompositeMap: a novel framework for music similarity measure
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Robust audio identification for MP3 popular music
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Effective music tagging through advanced statistical modeling
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Enhancing multi-label music genre classification through ensemble techniques
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval
ACM Transactions on Information Systems (TOIS)
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Combination
IEEE Transactions on Multimedia
Content-Based Information Fusion for Semi-Supervised Music Genre Classification
IEEE Transactions on Multimedia
Time Series Models for Semantic Music Annotation
IEEE Transactions on Audio, Speech, and Language Processing
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Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over the temporal dimension. From them, DMCM elucidates the concept dynamics in the music document, representing them with a novel music signature scheme called Stochastic Music Concept Histogram (SMCH) that captures the probability distribution over all the concepts. Experiment results with two large music collections confirm the advantages of the proposed framework over existing methods on various MIR tasks.