Music is traditionally retrieved by title, composer or subject classification. It is possible, with current technology, to retrieve music from a database on the basis of a few notes sung or hummed into a microphone. This paper describes the implementation of such a system, and discusses several issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input.

With the growth in digital representations of music, and of music stored in these representations, it is increasingly attractive to search collections of music. One mode of search is by similarity, but, for music, similarity search presents several difficulties: in particular, deciding what part of the music is likely to be perceived as the theme by a listener, and deciding whether two pieces of music with different sequences of notes represent the same theme. In this paper we propose a three-stage framework for matching pieces of music. We use the framework to compare a range of techniques for determining whether two pieces of music are similar, by experimentally testing their ability to retrieve different transcriptions of the same piece of music from a large collection of MIDI files. These experiments show that different comparison techniques differ widely in their effectiveness; and

In this paper we give an overview of four algorithms that we have developed for pattern matching, pattern discovery and data compression in multidimensional datasets. We show that these algorithms can fruitfully be used for processing musical data. In particular, we show that our algorithms can discover instances of perceptually signifrant musica 1 repetition that cannot be found using previous approaches. We also describe results that suggest the possibility of using our datacompression algorithm for modelling expert motivic-thematic music analysis.

Evaluating music information retrieval systems is acknowledged to be a difficult problem. We have created a database and a software testbed for the systematic evaluation of various query-by-humming (QBH) search systems. As might be expected, different queries and different databases lead to wide variations in observed search precision. "Natural" queries from two sources led to lower performance than that typically reported in the QBH literature.

Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained ” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity or distance metric, the search for similar segments, forming clusters of similar segments, and explaining music in terms of these clusters. Several transcription methods are considered: monophonic pitch estimation, chroma (spectral) representation, and polyphonic transcription followed by harmonic analysis. Also, several algorithms that search for similar segments are described. These techniques can be used to perform an analysis of musical structure, as illustrated by examples. 1.

Query-by-humming systems search a database of music for good matches to a sung, hummed, or whistled melody. Errors in transcription and variations in pitch and tempo can cause substantial mismatch between queries and targets. Thus, algorithms for measuring melodic similarity in query-by-humming systems should be robust. We compare several variations of search algorithms in an effort to improve search precision. In particular, we describe a new frame-based algorithm that significantly outperforms note-by-note algorithms in tests using sung queries and a database of MIDI-encoded music.

'Query by humming ' is an interaction concept in which the identity of a song has to be revealed fast and orderly from a given sung input using a large database of known melodies. In short, it tries to detect the pitches in a sung melody and compares these pitches with symbolic representations of the known melodies. Melodies that are similar to the sung pitches are retrieved. Approximate pattern matching in the melody comparison process compensates for the errors in the sung melody by using classical dynamic programming. A filtering method is used to save computation in the dynamic programming framework. This paper presents the algorithms for pitch detection, note onset detection, quantization, melody encoding and approximate pattern matching as they have been implemented in the CubyHum software system. Since human reproduction of melodies is imperfect, findings from an experimental singing study were a crucial input to the development of the algorithms. Future research should pay special attention to the reliable detection of note onsets in any preferred singing style. In addition, research on index methods and fast bitparallelism algorithms for approximate pattern matching need to be further pursued to decrease computational requirements when dealing with large melody databases. 1.

An important problem in computational music analysis is the representation and automated discovery of recurrent patterns. In this paper we present a new method for pattern representation and discovery in a large corpus of music. Using the formalism of multiple viewpoints, music is viewed as multiple streams of description derived from the basic surface representation. Patterns are discovered within viewpoint sequences derived from the corpus for selected viewpoints. A statistical method is used to restrict attention to only those patterns which occur much more frequently than expected, where expectation is based on a Markov model of viewpoint elements. The concept of the longest significant patterns in a corpus is introduced. The method presented in this paper is designed to rapidly enumerate all longest significant patterns within a large corpus. An application of the method to the Bach chorales is presented.

We represent music as sets of points or sets of horizontal line segments in the Euclidean plane. Via this geometric representation we cast transposition invariant content-based music retrieval problems as ones of matching sets of points or sets of horizontal line segments in plane under translations. For finding the exact occurrences of a point set (the query pattern) of size m within another point set (representing the database) of size n, we give an algorithm with running time O(mn), and for finding partial occurrences another algorithm with running time O(mn log m). We also use the total length of the overlap between the line segments...

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