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.
12580255
Comparison of Word and Subword Indexing Techniques for Mandarin Chinese Spoken Document Retrieval
In this paper, we investigate the use of words and subwords (including both characters and syllables) in audio indexing for Mandarin Chinese spoken document retrieval. Two retrieval approaches, including the well-known vector space model approach and the newly proposed HMM/Ngram-based approach, are used in the present work. We focus on the use of an entire Chinese textual story (from a newspaper) as a query to retrieve Mandarin Chinese spoken documents (from news broadcasts). Experiments are based on the Topic Detection and Tracking Corpora.
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.
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.
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.
10755440
Syllable-Based Chinese Text/Spoken Document Retrieval Using Text/Speech Queries
In light of the rapid growth of Chinese information resources on the Internet, this study investigates a novel approach that deals with the problem of Chinese text and spoken document retrieval using both text and speech queries. By properly utilizing the monosyllabic structure of the Chinese language, the proposed approach estimates the statistical similarity between the text/speech queries and the text/spoken documents at the phonetic level using the syllable-based statistical information. The investigation successfully implemented a prototype system with an interface supporting some user-friendly functions and the initial test results demonstrate the feasibility of the proposed approach.
2318961
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance
In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
16437816
Fast Computation of Reachability Labeling for Large Graphs
The need of processing graph reachability queries stems from many applications that manage complex data as graphs. The applications include transportation network, Internet traffic analyzing, Web navigation, semantic web, chemical informatics and bio-informatics systems, and computer vision. A graph reachability query, as one of the primary tasks, is to find whether two given data objects, u and v, are related in any ways in a large and complex dataset. Formally, the query is about to find if v is reachable from u in a directed graph which is large in size. In this paper, we focus ourselves on building a reachability labeling for a large directed graph, in order to process reachability queries efficiently. Such a labeling needs to be minimized in size for the efficiency of answering the queries, and needs to be computed fast for the efficiency of constructing such a labeling. As such a labeling, 2-hop cover was proposed for arbitrary graphs with theoretical bounds on both the construction cost and the size of the resulting labeling. However, in practice, as reported, the construction cost of 2-hop cover is very high even with super power machines. In this paper, we propose a novel geometry-based algorithm which computes high-quality 2-hop cover fast. Our experimental results verify the effectiveness of our techniques over large real and synthetic graph datasets.
6612123
Automatic Misconfiguration Troubleshooting with PeerPressure
Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. 
 
In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. 
 
In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.
1927942
Tracking and segmenting people with occlusions by a sample consensus based method
One of the most difficult issues in visual tracking is to track people in groups, especially under occlusions. In this paper, we present a novel sample consensus based method, which utilizes both color and spatial information of human bodies, to model the appearance of people. We use this appearance model to segment and track people through occlusions. We show experimental results in several video sequences to validate the effectiveness of the proposed method.
13221842
Rank-R approximation of tensors using image-as-matrix representation
We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in traditional dimensionality reduction techniques like PCA, and higher-dimensional data as a tensor. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. The challenges lie in the computational and memory requirements for large ensembles. Currently, there exists a rank-R approximation algorithm which, although applicable to any number of dimensions, is efficient for only low-rank approximations. For larger dimensionality reductions, the memory and time costs of this algorithm become prohibitive. We propose a novel algorithm, for rank-R approximations of third-order tensors, which is efficient for arbitrary R but for the important special case of 2D image ensembles, e.g. video. Both of these algorithms reduce redundancies present in all dimensions. Rank-R tensor approximation yields the most compact data representation among all known image-as-matrix methods. We evaluated the performance of our algorithm vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification.