10884708
Multi-way Hierarchic Classification of Musical Instrument Sounds
Musical instrument sounds can be classified in various ways, depending on the instrument or articulation classification. This paper presents a number of possible generalizations of musical instruments sounds classification which can be used to construct different hierarchical decision attributes. Each decision attribute will lead us to a new classifier and the same to a different system for automatic indexing of music by instrument sounds and their generalizations. Values of a decision attribute and their generalizations are used to construct atomic queries of a query language built for retrieving musical objects from MIR database (see http://www.mir.uncc.edu). When query fails, the cooperative strategy will try to find its lowest generalization which does not fail, taking into consideration all available hierarchical attributes. Thus, the music object representing most similar object in the database is returned as the query answer. This paper evaluates only two hierarchical attributes upon the same dataset which contains 2628 distinct musical samples of 102 instruments from McGill University Master Samples (MUMS) CD collection.
15778796
MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System
With the fast booming of online music repositories, there is a need for content-based automatic indexing which will help users to find their favorite music objects in real time. Recently, numerous successful approaches on musical data feature extraction and selection have been proposed for instrument recognition in monophonic sounds. Unfortunately, none of these methods can be successfully applied to polyphonic sounds. Identification of music instruments in polyphonic sounds is still difficult and challenging, especially when harmonic partials are overlapping with each other. This has stimulated the research on music sound separation and new features development for content-based automatic music information retrieval. Our goal is to build a cooperative query answering system (QAS), for a musical database, retrieving from it all objects satisfying queries like "find all musical pieces in pentatonic scale with a viola and piano where viola is playing for minimum 20 seconds and piano for minimum 10 seconds". We use the database of musical sounds, containing almost 4000 sounds taken from the MUMs (McGill University Master Samples), as a vehicle to construct several classifiers for automatic instrument recognition. Classifiers showing the best performance are adopted for automatic indexing of musical pieces by instruments. Our musical database has an FS-tree (Frame Segment Tree) structure representation. The cooperativeness of QAS is driven by several hierarchical structures used for classifying musical instruments.
6304926
Identifying bridging rules between conceptual clusters
A bridging rule in this paper has its antecedent and action from different conceptual clusters. We first design two algorithms for mining bridging rules between clusters in a database, and then propose two non-linear metrics for measuring the interestingness of bridging rules. Bridging rules can be distinct from association rules (or frequent itemsets). This is because (1) bridging rules can be generated by infrequent itemsets that are pruned in association rule mining; and (2) bridging rules are measured by the importance that includes the distance between two conceptual clusters, whereas frequent itemsets are measured by only the support.
15648497
Interactive browsing of 3D environment over the Internet
In this paper, we describe a system for wandering in a realistic environment over the Internet. The environment is captured by the concentric mosaic, compressed via the reference block coder (RBC), and accessed and delivered over the Internet through the virtual media (Vmedia) access protocol. Capturing the environment through the concentric mosaic is easy. We mount a camera at the end of a level beam, and shoot images as the beam rotates. The huge dataset of the concentric mosaic is then compressed through the RBC, which is specifically designed for both high compression efficiency and just-in-time (JIT) rendering. Through the JIT rendering function, only a portion of the RBC bitstream is accessed, decoded and rendered for each virtual view. A multimedia communication protocol -- the Vmedia protocol, is then proposed to deliver the compressed concentric mosaic data over the Internet. Only the bitstream segments corresponding to the current view are streamed over the Internet. Moreover, the delivered bitstream segments are managed by a local Vmedia cache so that frequently used bitstream segments need not be streamed over the Internet repeatedly, and the Vmedia is able to handle a RBC bitstream larger than its memory capacity. A Vmedia concentric mosaic interactive browser is developed where the user can freely wander in a realistic environment, e.g., rotate around, walk forward/backward and sidestep, even under a tight bandwidth of 33.6 kbps.
1297870
An active learning framework for content-based information retrieval
We propose a general active learning framework for content-based information retrieval. We use this framework to guide hidden annotations in order to improve the retrieval performance. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. During training, the learning algorithm samples objects in the database and presents them to the annotator to assign attributes. For each sampled object, each probability is set to be one or zero depending on whether or not the corresponding attribute is assigned by the annotator. For objects that have not been annotated, the learning algorithm estimates their probabilities with biased kernel regression. Knowledge gain is then defined to determine, among the objects that have not been annotated, which one the system is the most uncertain. The system then presents it as the next sample to the annotator to which it is assigned attributes. During retrieval, the list of probabilities works as a feature vector for us to calculate the semantic distance between two objects, or between the user query and an object in the database. The overall distance between two objects is determined by a weighted sum of the semantic distance and the low-level feature distance. The algorithm is tested on both synthetic databases and real databases of 3D models. In both cases, the retrieval performance of the system improves rapidly with the number of annotated samples. Furthermore, we show that active learning outperforms learning based on random sampling.
206618709
Building an Ontology for Financial Investment
Intelligent agent technologies hold great promise for the financial services and investment industries such as portfolio management. In financial investment, a multi-agent system approach is natural because of the multiplicity of information sources and the different expertise that must be brought to bear to produce a good recommendation (such as a stock buy or sell decision). The agents in a multi-agent system need to coordinate, cooperate or communicate with each other to solve a complex problem. However, ontologies are a key component in how different agents in a multi-agent system can communicate effectively, and how the knowledge of agents can develop. This paper presents a case study in building an ontology in financial investment. The lessons we learned from the construction process are discussed. Based on our ontology development experience and the current development of ontologies, a framework of next generation ontology construction tools, which is aimed to facilitate the ontology construction, is proposed.
11885669
A decision tree-based multimodal data mining framework for soccer goal detection
We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.