In this paper, we propose a musical feature extracted from the bit stream of AAC (Advanced Audio Coding) compressed audio data without decoding to audio signals. We focus on the spectral data which are stored in the bit stream for representing the flatten MDCT (Modified Discrete Cosine Transform) of an audio signal. For computing the musical feature, we extract the spectral data and apply the Discrete Wavelet Transform (DWT) to the extracted spectral data. For musical genre classification, we use the discriminant analysis as a classifier. We experimented on 1,498 AAC compressed audio data collected from 10 musical genres and evaluated the performance of the musical feature. We got the maximum correct ratios 81.24%. The experiments showed that the musical feature based on the spectral data in the bit stream had good performance for genre classification in the MPEG-4 AAC compressed domain.

We proposed a musical feature based on LSP (Line Spectrum Pair) parameter directly extracted from the bitstream in the MPEG-4 TwinVQ audio data. Our key idea is to extract the musical features by using information stored in the bitstream without decoding to audio signals. In this paper, we propose two musical features for musical genre classification of MPEG-4 TwinVQ audio data. For extracting musical features, we focus on LPC (Linear Predictive Coding) cepstrum and LPC coefficient computed from LSP parameter in the bitstream of TwinVQ audio data by inverse operations of encoding steps. The musical features based on LPC cepstrum and on LPC coefficient are computed by Discrete Wavelet Transform (DWT). For musical genre classification, we use the Discriminant Analysis (DA) as a classifier. We experimented on 2, 196 TwinVQ audio data collected from 10 musical genres and evaluated the performance of the musical features. From the experiments, we got the correct ratios 79.7% and 84.1% for LPC coefficient-based musical feature and LPC cepstrum-based musical feature, respectively. And we compared the performance of two musical features. The experiments showed that LPC cepstrum-based musical feature had good performance for musical genre classification in the compressed domain of MPEG-4 TwinVQ audio compression.

A method for phrasing music data into meaningful musical pieces (e.g., bar and phrase) is an important function to analyze music data. To realize this function, we propose a method for extracting a unit of music data (musical unit) in the compressed domain of TwinVQ audio compression (MPEG-4 audio). Our key idea is to extract a musical unit from a sequence of autocorrelation coefficients computed in the encoding step of TwinVQ audio compression. We call the sequence of the autocorrelation coefficients the "autocorrelation sequence r". We use the k-th autocorrelation sequence r/sub k/ (k=1, 2, ..., 20) of music data for extracting a musical unit of music data. First, we calculate the j/sub k/-th autocorrelation coefficient a/sub k//sup j//sub k/ of the k-th autocorrelation sequence r/sub k/ (j/sub k/=38, 39, ..., 208; k=1, 2, ...,20). Second, for detecting the peak in the sequence (a/sub k//sup 38/, a/sub k//sup 39/, ..., a/sub k//sup 208/), the Laplacian filter is applied to the sequence. We then obtain the order p/sub k/ for which the maximum differential coefficient is attained. Finally, we compute the musical unit using p/sub k/. To evaluate the performance of extracting the musical unit by our method, we collected 64 music data and obtained autocorrelation sequences by applying the TwinVQ encoder to each data. We then applied our extraction algorithm to each autocorrelation sequence. The experimental results reveal a very good performance in the extraction of a musical unit for phrasing music data.

The present paper describes a method for indexing a piece of music using the TwinVQ (Transform-domain Weighted Interleave Vector Quantization) audio compression (MPEG-4 audio standard). First, we present a framework for indexing a piece of music based on the autocorrelation coefficients computed in the encoding step of TwinVQ audio compression. Second, we propose a new music feature that is robust with respect to bit rate based on the fact that the i-th autocorrelation coefficient with bit rate B1 of a piece of music computed in the encoding step of TwinVQ audio compression can approximate the j-th autocorrelation coefficient with bit rate B2 of the piece of music where i= left lfloor frac B_1 B_2 j right rfloor, and on the wavelet transform. Finally, we perform retrieval experiments on 1,023 pieces of polyphonic music with bit rate (8 kbps, 12 kbps, 16 kbps, 20 kbps, 24 kbps, 28 kbps, 32 kbps, 36 kbps, 40 kbps, and 44 kbps). The experimental results indicate that the proposed music feature for indexing has excellent retrieval performance for queries of various bit rates.

Web-log mining is a technique to find "useful" information from access-log data. Typically, association rule mining is used to find frequent patterns (or sequence patterns) of visited pages from access logs and to build users' behavior models from those patterns. In this direction, there exists a difficulty that a human decision-maker must do such data mining process many times under different constraining conditions, different groups of pages, and different levels of abstraction. In order to support this process, this paper proposes a novel datacube model called itemset cube. This cube manages frequent itemsets under various conditions which are modeled by a n-dimensional space. An itemset cube is materialized, sliced, and rolled-up repeatedly in the same way as a traditional scalar datacube is done for interactive scalar-value analysis. Although this looks simple, fast execution of these operations on an itemset cube is difficult. It is because different cells in an itemset cube contain different numbers of records, but these cells must use the same threshold ratios in order to detect frequent itemsets of equal quality. In this paper, a datacube model for storing frequent itemsets is described, and then an efficient algorithm of associated operations is proposed. Its application to a real-life dataset is also demonstrated.

In this paper we propose an audio feature for TwinVQ audio retrieval. For making effective audio database, we consider that these two techniques (compression and feature extraction) are dealt with on one platform. The proposed audio feature satisfies the following requirements: 1) independent of bit rate; 2) extractable from compressed data without decoding; 3) computable in the framework of TwinVQ. We show that the autocorrelation coefficient is theoretically independent of bit rate and confirm experimentally that the feature computed from CD audio data is actually independent of bit rate.

It is shown that the idea of the successive refinement of interval partitions, which plays the key role in the interval algorithm for random number generation proposed by Han and Hoshi (see ibid., vol.43, p.599-611, 1997) is also applicable to the homophonic coding. An interval algorithm for homophonic coding is introduced which produces an independent and identically distributed (i.i.d.) sequence with probability p. Lower and upper bounds for the expected codeword length are given. Based on this, an interval algorithm for fixed-to-variable homophonic coding is established. The expected codeword length per source letter converges to H(X)/H(p) in probability as the block length tends to infinity, where H(X) is the entropy rate of the source X. The algorithm is asymptotically optimal. An algorithm for fixed-to-fixed homophonic coding is also established. The decoding error probability tends to zero as the block length tends to infinity. Homophonic coding with cost is generally considered. The expected cost of the codeword per source letter converges to c~H(X)/H(p) in probability as the block length tends to infinity, where, c~ denotes the average cost of a source letter. The main contribution of this paper can be regarded as a novel application of Elias' coding technique to homophonic coding. Intrinsic relations among these algorithms, the interval algorithm for random number generation and the arithmetic code are also discussed.

We propose a robust texture image retrieval using hierarchical relations between the decomposed sub-images based on the wavelet transform. The key idea is to use hierarchical correlations between the wavelet coefficients as texture features. We express the pyramidal structure of wavelet coefficients by associating the nodes of a complete quadtree with wavelet coefficients. We define a hierarchical dissimilarity vector between a parent node and his child, to express a hierarchical relation between them. Then, to describe a relation among child nodes, we compute a covariance matrix of dissimilarity vectors. We associate the covariance matrix with the parent node, and call such a quadtree "hierarchical correlation wavelet tree" (HCWT). Finally, to make up an index of database, we calculate the texture feature vectors defined by the diagonal of elements of the covariance matrix of HCWT and the texture feature vectors, and use the discriminant analysis to make an effective index from the texture feature vectors. For retrieving the similar images, we use the k-nearest neighbor search in the index space. Experiments showed a good performance of the proposed retrieval method.

Today, there are many Web information sources (WISs) that are suitable for mobile users. How/when to integrate such Web contents (which may be location-dependent contents) into a new simpler WIS for mobile users will become a problem, because different mobile users need different integration. A possible solution is to allow each individual mobile user to directly define, on his PDA, which pair of WISs should be integrated in what ways. Thereafter, system-side servers should materialize a definition (issued from a user's browser) as a new WIS. For this purpose, the paper introduces a new style of integration called navigational integration of WISs, and then describes a system architecture.

The problem of generating a random number with an arbitrary probability distribution by using a general biased M-coin is studied. An efficient and very simple algorithm based on the successive refinement of partitions of the unit interval (0, 1), which we call the interval algorithm, is proposed. A fairly tight evaluation on the efficiency is given. Generalizations of the interval algorithm to the following cases are investigated: (1) output sequence is independent and identically distributed (i.i.d.); (2) output sequence is Markov; (3) input sequence is Markov; (4) input sequence and output sequence are both subject to arbitrary stochastic processes.

The construction problems of optimum sequence trees (or digital search trees) are considered in the following frameworks: 1. construction of optimum trees from a set of keys, 2. transformation of an arbitrary tree into an optimum one, 3. optimum insertions of keys into an optimum tree, 4. optimum deletions of keys from an optimum tree. Algorithms are shown. The number of operations needed for the algorithm in the framework 1 is at most $O(N^{2}L)$, and in the framework 2 it is at most $O(N^{3}L)$ both with $O(N)$ storage locations, where N and L are the number of keys and the length of coded keys respectively. Necessary and sufficient conditions for the optimality of sequence trees are also given.