This paper extends the familiar “query by humming ” music retrieval framework into the polyphonic realm. As humming in multiple voices is quite difficult, the task is more accurately described as “query by audio example”, onto a collection of scores. To our knowledge, we are the first to use polyphonic audio queries to retrieve from polyphonic symbolic collections. Furthermore, as our results will show, we will not only use an audio query to retrieve a known-item symbolic piece, but we will use it to retrieve an entire set of real-world composed variations on that piece, also in the symbolic format. The harmonic modeling approach which forms the basis of this work is a new and valuable technique which has both wide applicability and future potential.� 1.

This paper outlines the findings-to-date of a project to assist in the efforts being made to establish a TREC-like evaluation paradigm within the Music Information Retrieval (MIR) research community. The findings

A huge amount of audio data is accessible to everyone by on-line or off-line information services and it is necessary to develop techniques to automatically describe and deal with this data in a meaningful way. In the particular context of music content processing it is important to take into account the melodic aspects of the sound. The goal of this article is to review the different techniques proposed for melodic description and extraction. Some ideas around the concept of melody are first presented. Then, an overview of the different ways of describing melody is done. As a third step, an analysis of the methods proposed for melody extraction is made, including pitch detection algorithms. Finally, techniques for melodic pattern induction and matching are also studied, and some useful melodic transformations are reviewed. 1

Many applications depend on efficient management of large sets of distinct strings in memory. For example, during index construction for text databases a record is held for each distinct word in the text, containing the word itself and information such as counters. We propose a new data structure, the burst trie, that has significant advantages over existing options for such applications: it requires no more memory than a binary tree; it is as fast as a trie; and, while not as fast as a hash table, a burst trie maintains the strings in sorted or near-sorted order. In this paper we describe burst tries and explore the parameters that govern their performance. We experimentally determine good choices of parameters, and compare burst tries to other structures used for the same task, with a variety of data sets. These experiments show that the burst trie is particularly effective for the skewed frequency distributions common in text collections, and dramatically outperforms all other data structures for the task of managing strings while maintaining sort order.

We present an efficient prototype for music information retrieval. The prototype uses bitparallel algorithms for locating transposition invariant matches of monophonic query melodies within monophonic or polyphonic music stored in a database. When dealing with monophonic music, we employ a fast approximate bit-parallel algorithm with special edit distance metrics.

In this paper, the problem of processing audio signals is addressed in the context of query-by-humming systems. Since singing is naturally used as input, we aim to develop a front end dedicated to the symbolic translation of voice into a sequence of pitch and duration pairs. This operation is crucial for the effectiveness of searching for music by melodic similarity. In order to identify and segment a tune, well-known signal processing techniques are applied to the singing voice. After detecting pitch, a novel postprocessing stage is proposed to adjust the intonation of the user. A global refinement is based on a relative scale estimated out of the most frequent errors made by singers. Four rules are then employed to eliminate local errors. This front end has been tested with five subjects and four short tunes, detecting some 90% of right notes. Results have been compared to other approximation methods like rounding to the nearest absolute tone/interval and an example of adaptive moving tuning, achieving respectively 74%, 80% and 44% of right estimations. A special session of tests has been conducted to verify the capability of the system in detecting vibrato/legato notes. Finally, issues about the best representation for the translated symbols are briefly discussed. 1.

The content-based retrieval of Western music has received increasing attention recently. Much of this research deals with monophonic music. Polyphonic music is more common, but also more difficult to represent. Music information retrieval systems must extract viable features before they can define similarity measures. We summarize and categorize representation features that have been used for polyphonic retrieval with the aim of laying standardized groundwork for future feature extraction research. Comparisons with and extensions to monophonic approaches are given, and a new feature, an extension of duration-independent pitch slices, is proposed. Key words: Music retrieval, polyphonic features 1.

The emergence of digital music on the Internet requires new information retrieval methods adapted to the specific characteristics and needs. While music retrieval based on the text information, such as title, composers, or subject classification, has been implemented in many existing systems, retrieval of a piece of music based on music contents, especially based on an incomplete, imperfect recall of a fragment of the music, has not yet been fully explored. This thesis is to explore the main problems involved in a web-based melody retrieval system. I propose to build a query-by-humming system, which can find a piece of music in the digital music repository based on a few hummed notes, using a melody representation that combines both the pitch contour and the beat information. Since an input query (hummed melody) may have various errors due to uncertainty of the user's memory or the user's singing ability, the system should be able to tolerate the errors. Furthermore, extracting m...

Recent interest in the area of music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of recent developments in statistical modeling. In this paper we discuss an application of Random Fields to the problem of creating accurate yet flexible statistical models of polyphonic music. With such models in hand, the challenges of developing e#ective searching, browsing and organization techniques for the growing bodies of music collections may be successfully met. We o#er an evaluation of these models in terms of perplexity and prediction accuracy, and show that random fields not only outperform Markov chains, but are much more robust in terms of overfitting.

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