Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Cooperative Answering through Controlled Query Relaxation
IEEE Expert: Intelligent Systems and Their Applications
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Analysis of Sound Features for Music Timbre Recognition
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Maximum Likelihood Study for Sound Pattern Separation and Recognition
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Multi-way Hierarchic Classification of Musical Instrument Sounds
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Knowledge discovery-based identification of musical pitches and instruments in polyphonic sounds
Engineering Applications of Artificial Intelligence
EURASIP Journal on Applied Signal Processing
Sound Isolation by Harmonic Peak Partition For Music Instrument Recognition
Fundamenta Informaticae - Special issue ISMIS'05
Harmonic source separation using prestored spectra
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Musical source separation using time-frequency source priors
IEEE Transactions on Audio, Speech, and Language Processing
Learning from Soft-Computing Methods on Abnormalities in Audio Data
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
On Reaching Consensus by a Group of Collaborating Agents
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Multi-label automatic indexing of music by cascade classifiers
Web Intelligence and Agent Systems
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The high volume of digital music recordings in the internet repositories has brought a tremendous need for a cooperative recommendation system to help users to find their favorite music pieces. Music instrument identification is one of the important subtasks of a content-based automatic indexing, for which authors developed novel new temporal features and built a multi-hierarchical decision system S with all the low-level MPEG7 descriptors as well as other popular descriptors for describing music sound objects. The decision attributes in S are hierarchical and they include Hornbostel-Sachs classification and generalization by articulation. The information richness hidden in these descriptors has strong implication on the confidence of classifiers built from S. Rule-based classifiers give us approximate definitions of values of decision attributes and they are used as a tool by content-based Automatic Indexing Systems (AIS). Hierarchical decision attributes allow us to have the indexing done on different granularity levels of classes of music instruments. We can identify not only the instruments playing in a given music piece but also classes of instruments if the instrument level identification fails. The quality of AIS can be verified using precision and recall based on two interpretations: user and system-based [16]. AIS engine follows system-based interpretation.