2116197
Search Effectiveness Measures for Symbolic Music Queries in Very Large Databases
In the interest of establishing robust benchmarks for search efficiency, we conducted a series of tests on symbolic databases of musical incipits and themes taken from several diverse kinds of repertories. The results we report differ from existing studies in four respects: (1) the data quantity is much larger (c. 100,000 entries in all); (2) the levels of melodic precision are more refined; (3) anchored and unanchored searches were differentiated; and (4) results from combination pitchand-rhythm searches were compared with those for pitch-only searches.
12371631
Multiple watermarking: is power sharing better than time sharing?
Writing on dirty paper, a mathematical model for watermarking, is examined and extended to allow simultaneous watermarking at multiple rates. Based on the extended model, it is argued that the optimal watermarking strategy is to share the power. The argument is further supported by simulations of joint spread spectrum and quantization watermarking.
6989862
Using Prosody for Automatic Sentence Segmentation of Multi-party Meetings
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter) We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR) We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.
39241507
Feature selection using a proximity-index optimization model
Abstract One of the recurring issues in pattern recognition problems has been feature extraction and selection. This paper addresses this issue from a different pespective. Without assuming any particular classificaiton algorithm, it first suggests that one extract as much information as conveniently possible in several pattern-information domains. This paper later suggests applying the proposed Proximity-Index method, to select a significantly smaller, yet optimal feature subset. This method is formally described and is successfully applied to a waveform classification problem. The features selected by the algorithm are used to classify ten signal classes and produce a very encouraging recognition performance of 87.00% on 200 samples. This method is computationally inexpensive and particularly useful for large data set problems.
6201806
Loop optimization for aggregate array computations
An aggregate array computation is a loop that computes accumulated quantities over array elements. Such computations are common in programs that use arrays, and the array elements involved in such computations often overlap, especially across iterations of loops, resulting in significant redundancy in the overall computation. The paper presents a method and algorithms that eliminate such overlapping aggregate array redundancies and shows both analytical and experimental performance improvements. The method is based on incrementalization, i.e., updating the values of aggregate array computations from iteration to iteration rather than computing them from scratch in each iteration. This involves maintaining additional information not maintained in the original program. The authors reduce various analysis problems to solving inequality constraints on loop variables and array subscripts, and they apply results from work on array data dependence analysis. Incrementalizing aggregate array computations produces drastic program speedup compared to previous optimizations. Previous methods for loop optimizations of arrays do not perform incrementalization, and previous techniques for loop incrementalization do not handle arrays.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
889762
Unresponsive flows and AQM performance
Routers handle data packets from sources unresponsive to TCP's congestion avoidance feedback. We are interested in the impact these sources have on active queue management (AQM) control of long-lived TCP traffic. In this paper, we combine models of TCP/AQM dynamics with models of unresponsive traffic to analyze the effects on AQM performance.