9768130
Maximum Likelihood Study for Sound Pattern Separation and Recognition
The increasing needs of content-based automatic indexing for large musical repositories have led to extensive investigation in musical sound pattern recognition. Numerous acoustical sound features have been developed to describe the characteristics of a sound piece. Many of these features have been successfully applied to monophonic sound timbre recognition. However, most of those features failed to describe enough characteristics of polyphonic sounds for the purpose of classification, where sound patterns from different sources are overlapping with each other. Thus, sound separation technique is needed to process polyphonic sounds into monophonic sounds before feature extraction. In this paper, we proposed a novel sound source separation and estimation system to isolate sound sources by maximum likelihood fundamental frequency estimation and pattern matching of a harmonic sequence in our feature database.
9156106
Blind Signal Separation of Similar Pitches and Instruments in a Noisy Polyphonic Domain
In our continuing work on ”Blind Signal Separation” this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms.
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.
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.
40464778
A model for compressing probabilities in belief networks
Probabilistic reasoning with belief (Bayesian) networks is based on conditional probability matrices. Thus it suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary for the computations is often overwhelming. So, compressing the conditional probability table is one of the most important issues faced by the probabilistic reasoning community. Santos suggested an approach (called linear potential functions) for compressing the information from a combinatorial amount to roughly linear in the number of random variable assignments. However, much of the information in Bayesian networks, in which there are no linear potential functions, would be fitted by polynomial approximating functions rather than by reluctantly linear functions. For this reason, we construct a polynomial method to compress the conditional probability table in this paper. We evaluated the proposed technique, and our experimental results demonstrate that the approach is efficient and promising.
17966080
User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this paper, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested under a couple of conditions.
33661341
Cycles of each length in tournaments
Let G = (V,A) be a digraph of order n. Digraph G is said to be arc-k-cyclic if each arc of G is contained in a cycle of length k (3 ≤ k ≤ n). An arc e of G is said to be pancyclic if e is contained in cycles of all lengths m, 3 ≤ m ≤ n. Digraph G is said to be arc-pancyclic if each arc of G is pancyclic. The main results of this paper are that (1) except for a certain family of counterexamples, every arc-3-cyclic tournament is arc-pancyclic; (2) at most one arc of an arc-3-cyclic tournament is not pancyclic.
21282948
Information-Based Cooperation in Multiple Agent Systems
In this paper, we divide the cooperation in multiple agent systems into task-based cooperation and information-based cooperation. We describe information-based cooperation as information synthesizing and decision making. To implement information synthesizing, agents' beliefs are decomposed into two levels. The higher level represents the possible information, and the lower level subsequently estimates a number function for the belief by synthesizing the possible information.
5200887
Damage pattern mining in hurricane image databases
We present a damage pattern mining framework for hurricane data on residential houses using aerial photographs with 1:3000 based scale. The vertical photographs are normally collected for an overview of a disaster area or for more detailed assessment. Damage on roof, especially for shingles torn off, is expected to be discovered in a more efficient and automatic way instead of going through the high-resolution aerial photograph for numerous details. The discovered damage patterns can then be used for hurricane damage assessment and impacts on different geographical areas. Our methodology is: (1) applying a novel and effective segmentation method on each residential house on the aerial photograph of one community, (2) using the segmentation results to obtain a set of indexing parameters for each house representing the damage level of roof cladding as well as the patterns of damage, (3) using these parameters to select several templates representing the damage patterns so that users can issue query-by-example (QBE) queries. The proposed segmentation method is an unsupervised simultaneous partition and class parameter estimation algorithm that considers the problem of segmentation as a joint estimation of the partition and class parameter variables. By utilizing this segmentation method, the indexing parameters can be obtained automatically. The QBE capability can assist in finding similar damage patterns on the roof of the residential houses in different locations in the image databases. Experiments based on the aerial photographs of Hurricane Andrew in 1992 are conducted and analyzed to show the effectiveness of the proposed hurricane damage pattern mining framework.