7772067
Extractive Chinese Spoken Document Summarization Using Probabilistic Ranking Models
The purpose of extractive summarization is to automatically select indicative sentences, passages, or paragraphs from an original document according to a certain target summarization ratio, and then sequence them to form a concise summary. In this paper, in contrast to conventional approaches, our objective is to deal with the extractive summarization problem under a probabilistic modeling framework. We investigate the use of the hidden Markov model (HMM) for spoken document summarization, in which each sentence of a spoken document is treated as an HMM for generating the document, and the sentences are ranked and selected according to their likelihoods. In addition, the relevance model (RM) of each sentence, estimated from a contemporary text collection, is integrated with the HMM model to improve the representation of the sentence model. The experiments were performed on Chinese broadcast news compiled in Taiwan. The proposed approach achieves noticeable performance gains over conventional summarization approaches.
24553
Mandarin-English Information (MEI): investigating translingual speech retrieval
Abstract This paper describes the Mandarin–English Information (MEI) project, where we investigated the problem of cross-language spoken document retrieval (CL-SDR), and developed one of the first English–Chinese CL-SDR systems. Our system accepts an entire English news story (text) as query, and retrieves relevant Chinese broadcast news stories (audio) from the document collection. Hence, this is a cross-language and cross-media retrieval task. We applied a multi-scale approach to our problem, which unifies the use of phrases, words and subwords in retrieval. The English queries are translated into Chinese by means of a dictionary-based approach, where we have integrated phrase-based translation with word-by-word translation. Untranslatable named entities are transliterated by a novel subword translation technique. The multi-scale approach can be divided into three subtasks – multi-scale query formulation, multi-scale audio indexing (by speech recognition) and multi-scale retrieval. Experimental results demonstrate that the use of phrase-based translation and subword translation gave performance gains, and multi-scale retrieval outperforms word-based retrieval.
16518536
Automatic singer recognition of popular music recordings via estimation and modeling of solo vocal signals
In this paper, we investigate the problem of automatic singer identification, detection and tracking in popular music recordings with one or multiple singers. This problem reflects an important issue in multimedia applications that require the transcription and indexing of music data to meet the increasing demand for content-based information retrieval. The major challenges for this study arise from the fact that a singer's voice tends to be arbitrarily altered from time to time and is inextricably intertwined with the signal of the background accompaniment. To determine who is singing, or whether or when a particular singer is present in a music recording, methods are presented for separating vocal from nonvocal regions, for isolating singers' vocal characteristics from background music, and for distinguishing singers from one another. Experimental evaluations conducted on a pop music database consisting of solo and duet tracks confirm the validity of the proposed methods.
2984995
Query-By-Example Technique for Retrieving Cover Versions of Popular Songs with Similar Melodies
Retrieving audio material based on audio queries is an important and challenging issue in the research field of content-based access to popular music. As part of this research field, we present a preliminary investigation into retrieving cover versions of songs specified by users. The technique enables users to listen to songs with an identical tune, but performed by different singers, in different languages, genres, and so on. The proposed system is built on a query-by-example framework, which takes a fragment of the song submitted by the user as input, and returns songs similar to the query in terms of the main melody as output. To handle the likely discrepancies, e.g., tempos, transpositions, and accompaniments between cover versions and the original song, methods are presented to remove the non-vocal portions of the song, extract the sung notes from the accompanied vocals, and compare the similarities between the sung note sequences.
34060618
A Minimum Boundary Error Framework for Automatic Phonetic Segmentation
This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.
15210668
A Music Retrieval System Based on Query-by-Singing for Karaoke Jukebox
This paper investigates the problem of retrieving Karaoke music by singing. The Karaoke music encompasses two audio channels in each track: one is a mix of vocal and background accompaniment, and the other is composed of accompaniment only. The accompaniments in the two channels often resemble each other, but are not identical. This characteristic is exploited to infer the vocal’s background music from the accompaniment-only channel, so that the main melody underlying the vocal signals can be extracted more effectively. To enable an efficient and accurate search for a large music database, we propose a phrase onset detection method based on Bayesian Information Criterion (BIC) for predicting the most likely beginning of a sung query, and adopt a multiple-level multiple-pass Dynamic Time Warping (DTW) for melody similarity comparison. The experiments conducted on a Karaoke database consisting of 1,071 popular songs show the promising results of query-by-singing retrieval for Karaoke music.
16600904
A services oriented framework for next generation data analysis centers
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns and models from such large volumes of data. Although inexpensive storage is conducive to maintaining said data, accessing and managing it for knowledge discovery and data mining becomes a performance issue when datasets are large, dynamic, and distributed. In this work, we present our vision of a software framework consisting of middleware services to support interactive data mining over dynamic data at data analysis centers built on top of heterogeneous clusters. The design of a sampling service for dynamic data, together with initial performance results, are also presented.
44405113
Data hiding techniques for printed binary images
The objective of this research is to develop a method to hide information inside a binary image by digital halftoning techniques with certain modifications. Two modified digital halftoning techniques, modified ordered dithering and modified multiscale error diffusion, are used in this research. The data is encoded pixel by pixel in the halftone image according to position at the image and sequence of binarization, respectively. The eye model and mean square error are used to measure the image quality. A computer vision method has been developed to recognize the printed binary image. The results show that thousands of binary images similar to human vision but quite distinct from each other by computer vision can be generated. The eye model and computer vision are useful for both binary image quality measurement and data recognition. These new techniques have great potential in printing security documents such as currency, ID card as well as confidential documents.
4332805
Stable egress route selection for interdomain traffic engineering: model and analysis
We present a general model of interdomain route selection to study interdomain traffic engineering. In this model, the routing of multiple destinations can be coordinated. Thus the model can capture general traffic engineering behaviors such as load balancing and link capacity constraints. We first identify potential routing instability and inefficiency of interdomain traffic engineering. We then derive a sufficient condition to guarantee convergence. We also show that the constraints on local policies imposed by business considerations in the Internet can guarantee stability without global coordination. Using realistic Internet topology, we evaluate the extent to which routing instability of interdomain traffic engineering can happen when the constraints are violated
5368193
Progressive modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.
10403489
On reducing classifier granularity in mining concept-drifting data streams
Many applications use classification models on streaming data to detect actionable alerts. Due to concept drifts in the underlying data, how to maintain a model's up-to-dateness has become one of the most challenging tasks in mining data streams. State of the art approaches, including both the incrementally updated classifiers and the ensemble classifiers, have proved that model update is a very costly process. In this paper, we introduce the concept of model granularity. We show that reducing model granularity will reduce model update cost. Indeed, models of fine granularity enable us to efficiently pinpoint local components in the model that are affected by the concept drift. It also enables us to derive new components that can easily integrate with the model to reflect the current data distribution, thus avoiding expensive updates on a global scale. Experiments on real and synthetic data show that our approach is able to maintain good prediction accuracy at a fraction of model updating cost of state of the art approaches.