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.
7308728
Fluent speech prosody: Framework and modeling
Abstract The prosody of fluent connected speech is much more complicated than concatenating individual sentence intonations into strings. We analyzed speech corpora of read Mandarin Chinese discourses from a top–down perspective on perceived units and boundaries, and consistently identified speech paragraphs of multiple phrases that reflected discourse rather than sentence effects in fluent speech. Subsequent cross-speaker and cross-speaking-rate acoustic analyses of identified speech paragraphs revealed systematic cross-phrase prosodic patterns in every acoustic parameter, namely, F0 contours, duration adjustment, intensity patterns, and in addition, boundary breaks. We therefore argue for a higher prosodic node that governs, constrains, and groups phrases to derive speech paragraphs. A hierarchical multi-phrase framework is constructed to account for the governing effect, with complimentary production and perceptual evidences. We show how cross-phrase F0 and syllable duration patterns templates are derived to account for the tune and rhythm characteristic to fluent speech prosody, and argue for a prosody framework that specifies phrasal intonations as subjacent sister constituent subject to higher terms. Output fluent speech prosody is thus cumulative results of contributions from every prosodic layer. To test our framework, we further construct a modular prosody model of multiple-phrase grouping with four corresponding acoustic modules and begin testing the model with speech synthesis. To conclude, we argue that any prosody framework of fluent speech should include prosodic contributions above individual sentences in production, with considerations of its perceptual effects to on-line processing; and development of unlimited TTS could benefit most appreciably by capturing and including cross-phrase relationships in prosody modeling.
18351320
Spatial data query support in peer-to-peer systems
The distributed hash table (DHT) mechanisms have been proposed to manage data in very large, structured peer-to-peer (P2P) systems. DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and are hence extremely scalable. However, range queries - which are very common with spatial data - cannot be executed efficiently in these systems because the adoption of uniform hash functions to ensure excellent load balancing unfortunately destroys any existing data locality. In this report we propose a novel technique to preserve spatial locality information while also keeping some of the load balancing properties of DHT based systems. We describe our design as an extension of content-addressable networks (CAN) and illustrate the feasibility of supporting spatial range queries.
5266558
Resilient peer-to-peer streaming
We consider the problem of distributing "live" streaming media content to a potentially large and highly dynamic population of hosts. Peer-to-peer content distribution is attractive in this setting because the bandwidth available to serve content scales with demand. A key challenge, however, is making content distribution robust to peer transience. Our approach to providing robustness is to introduce redundance; both in network paths and in data. We use multiple, diverse distribution trees to provide redundancy in network paths and multiple description coding (MDC) to provide redundancy in data. We present a simple tree management algorithm that provides the necessary path diversity and describe an adaptation framework for MDC based on scalable receiver feedback. We evaluate these using MDC applied to real video data coupled with real usage traces from a major news site that experienced a large flash crowd for live streaming content. Our results show very significant benefits in using multiple distribution trees and MDC, with a 22 dB improvement in PSNR in some cases.
4473346
Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction
A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.
7336686
Online aggregation
Aggregation in traditional database systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements. These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.
44354759
Binary trees with the largest number of subtrees
This paper characterizes binary trees with n leaves, which have the greatest number of subtrees. These binary trees coincide with those which were shown by Fischermann et al. [Wiener index versus maximum degree in trees, Discrete Appl. Math. 122(1-3) (2002) 127-137] and Jelen and Triesch [Superdominance order and distance of trees with bounded maximum degree, Discrete Appl. Math. 125 (2-3) (2003) 225-233] to minimize the Wiener index.