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.
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.
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.
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.
8726769
A Prototypes-Embedded Genetic K-means Algorithm
This paper presents a genetic algorithm (GA) for K-means clustering. Instead of the widely applied string-of-group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means algorithm is used as the mutation operator. Hence, the proposed GA is called the prototypes-embedded genetic K-means algorithm (PGKA). With the inherent evolution process of evolutionary algorithms, PGKA has superior performance than the classical K-means algorithm, while comparing to other GA-based approaches, PGKA is more efficient and suitable for large scale data sets
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.
17118926
Background Subtraction Based on a Robust Consensus Method
Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance
134895
TCOZ approach to semantic web services design
Complex Semantic Web (SW) services may have intricate data state, autonomous process behavior and concurrent interactions. The design of such SW service systems requires precise and powerful modelling techniques to capture not only the ontology domain properties but also the services' process behavior and functionalities. In this paper we apply an integrated formal modeling language, Timed Communicating Object Z (TCOZ), to design SW services. Furthermore, the paper presents the development of the systematic translation rules and tools which can automatically extract the SW ontology and services semantic markup from the formal TCOZ design model.
10510054
Words in the History of a Turing Machine with a Fixed Input
The usual decision (halting or printing) problems for Turing machines (T.M.) are relative to an infinite class of them or an infinite class of inputs or1 a single T.M. We consider now a new type of problem (the consequence problem) con-neeted with a single input on a single T.M. It is known that in a definite sense everything which an erasing T.3{. can do can also be done by a nonerasing T.M., e.g., the halting problem is undeeidable in both bases. It turns out that for the consequence problem erasing does make an essential difference: decidable without erasing, but undecidable Mth erasing. From the undeeidability of the consequence problem for a T.M., it also follows that the same problem is undeeidable for certain simple monogenie normal systems. Consider a fixed finite alphabet A including a symbol b for the blank squares on a tape. For definiteness, we may assume that A = {b, ,, a}. Assume some familiar formulation of Turing machines. Definition. The word at any moment t in the history of a T.M. is the string consisting of the contents of the squares in the minimum block on the tape at t that includes all the marked squares and the square scanned at the initial moment (the origin). THEOREM 1. For arty fixed (finite) word at the initial moment, we can find a T.M. such that the set of words in its subsequent history is not ~cursive. Ullian shows [1] that there exists some nonrecursive but recursively enumerable set S such that there is a general recursive function f such that S = {f(0), f(f(O)), f(f(f(O))),-.. }. We can now give a T.M. which first erases the finitely many marks on the initial tape and returns to the origin, then it puts down the representation of 0 on tape, calculates the value of f(0), erasing everything else except the representation of f(0), calculates f2(0). In particular, we can do it in such a way that the symbol a is used, after the initial tape content is erased, only to mark f(0), f2(0), etc; it is erased before we evaluate f(f (0)) from f(0). Moreover, the string of a followed by the representation of f(0) always begins at the origin. Clearly the set of words in the history of this T.M. cannot be recursive because otherwise the set S would be recursive, whether a 'gd(0) ' belongs to the …
14473430
An optimal shape encoding scheme using skeleton decomposition
This paper presents an operational rate-distortion (ORD) optimal approach for skeleton-based boundary encoding. The boundary information is first decomposed into skeleton and distance signals, by which a more efficient representation of the original boundary results. Curves of arbitrary order are utilized for approximating the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals and for all boundaries within a frame, so that the overall distortion is minimized. The problem is solved with the use of Lagrangian relaxation and a shortest path algorithm in a 4D directed acyclic graph (DAG) we propose. By defining a path selection pattern, we reduce the computational complexity of the 4D DAG shortest path algorithm from O(N/sup -5/) to O(N/sup -4/), where N is the number of admissible control points for a skeleton. A suboptimal solution is also presented for further reducing the computational complexity of the algorithm to O(N/sup -2/). The proposed algorithm outperforms experimentally other competing algorithms.
1853880
Near-Neighbor Search in Pattern Distance Spaces
In this paper, we study the near-neighbor problem based on pattern similarity, a new type of similarity which conventional distance metrics such as Lp norm cannot model effectively. The problem, however, is important to many applications. For example, in DNA microarray analysis, the expression levels of two closely related genes may rise and fall under different external conditions or at different time. Although the magnitude of their expression levels may not be close, the patterns they exhibit over the time or under different conditions can be very similar. In this paper, we measure the distance between two objects by pattern similarity, i.e., whether the two objects exhibit a synchronous pattern of rise and fall under different conditions. We then present an efficient algorithm for near-neighbor search based on pattern similarity, and we perform tests on several real and synthetic data sets to show its effectiveness.