Melody recognition with learned edit distances

In a music recognition task, the classification of a new melody is often achieved by looking for the closest piece in a set of already known prototypes. The definition of a relevant similarity measure becomes then a crucial point. So far, the edit distance approach with a-priori fixed operation costs has been one of the most used to accomplish the task. In this paper, the application of a probabilistic learning model to both string and tree edit distances is proposed and is compared to a genetic algorithm cost fitting approach. The results show that both learning models outperform fixed-costs systems, and that the probabilistic approach is able to describe consistently the underlying melodic similarity model.This work was funded by the French ANR Marmota project, the Spanish PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018), and the Pascal Network of Excellence

Data-based melody generation through multi-objective evolutionary computation

Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte [FPU fellowship AP2012-0939]; and the Spanish Ministerio de Economía y Competitividad project TIMuL supported by UE FEDER funds [No. TIN2013–48152–C2–1–R]

Multimedia resource discovery

This chapter examines the challenges and opportunities of Multimedia Information Retrieval and corresponding search engine applications. Computer technology has changed our access to information tremendously: We used to search authors or titles (which we had to know) in library cards in order to locate relevant books; now we can issue keyword searches within the full text of whole book repositories in order to identify authors, titles and locations of relevant books. What about the corresponding challenge of finding multimedia by fragments, examples and excerpts? Rather than asking for a music piece by artist and title, can we hum its tune to find it? Can doctors submit scans of a patient to identify medically similar images of diagnosed cases in a database? Can your mobile phone take a picture of a statue and tell you about its artist and significance via a service that it sends this picture to? In an attempt to answer some of these questions we get to know basic concepts of multimedia resource discovery technologies for a number of different query and document types: piggy-back text search, i.e., reducing the multimedia to pseudo text documents; automated annotation of visual components; content-based retrieval where the query is an image; and fingerprinting to match near duplicates. Some of the research challenges are given by the semantic gap between the simple pixel properties computers can readily index and high-level human concepts; related to this is an inherent technological limitation of automated annotation of images from pixels alone. Other challenges are given by polysemy, i.e., the many meanings and interpretations that are inherent in visual material and the corresponding wide range of a user’s information need. This chapter demonstrates how these challenges can be tackled by automated processing and machine learning and by utilising the skills of the user, for example through browsing or through a process that is called relevance feedback, thus putting the user at centre stage. The latter is made easier by “added value” technologies, exemplified here by summaries of complex multimedia objects such as TV news, information visualisation techniques for document clusters, visual search by example, and methods to create browsable structures within the collection

Системы query-by-humming: обзор подходов и схема платформы для экспериментов

We give a survey of existing query-by-humming information retrieval systems that use feature extraction. The paper contains a complete description of the most popular feature extraction techniques (CENS and MFCC) and offers a design of an experimental system aimed at comparing different feature sets, vocabulary selection, indexing, and search methods.В работе даётся обзор существующих систем query-by-humming (поиска музыкальных композиций по напетому пользователем фрагменту), использующих извлечение признаков. Кроме того, даётся полное описание наиболее популярных методов извлечения признаков (CENS и MFCC), а также предлагается схема системы для проведения экспериментов, направленных на сравнение разных наборов признаков, методов построения словаря, индексации и поиска

Stylometry and Automatic Attribution of Medieval Liturgical Monodies

While automatic attribution of literary text as well as stylometry evaluation are nowadays well-established research areas, much less has been done in the field of musicology. Here we present the results of the implementation of an automatic stylometric attribution technique to a corpus of liturgical monodies of medieval origin (the so-called Gregorian Chant, Old Roman Chant and Ambrosian Chant). The ‘unidimensional’ nature of the musical repertoires investigated (rhythm-free melody without accompaniment) allows the adoption of a known method based on a pseudo-distance between frequency-vectors of n-gram of consecutive symbols. Finally, we show that some specific features of musicological interest inside the three liturgical families can be naturally extracted using a statistical analysis of n-gram distributions. The results presented show that a quantitative approach is well suited to support and accompany the investigation of refined problems in musicology

Robust polyphonic music retrieval with n-grams

In this paper we investigate the retrieval performance of monophonic and polyphonic queries made on a polyphonic music database. We extend the n-gram approach for full-music indexing of monophonic music data to polyphonic music using both rhythm and pitch information. We define an experimental framework for a comparative and fault-tolerance study of various n-gramming strategies and encoding levels. For monophonic queries, we focus in particular on query-by-humming systems, and for polyphonic queries on query-by-example. Error models addressed in several studies are surveyed for the fault-tolerance study. Our experiments show that different n-gramming strategies and encoding precision differ widely in their effectiveness. We present the results of our study on a collection of 6366 polyphonic MIDI-encoded music pieces

Automated Music Genre Classification Based on Analyses of Web-Based Documents and Listeners' Organizational Schemes

This paper describes a two-part study attempting to correlate music genre assignments performed by two primary, yet disparate groups: the music industry and consumers of popular music. An online survey was conducted, aimed at evaluating the latter group's perception of music genre. The sample of the survey consisted of 15 UNC-CH students affiliated with the music department. Concurrently, a series of genre classification experiments were conducted on several corpora of music reviews harvested from authoritative, online review websites. Results of the survey were subsequently triangulated with a portion of the music review corpora in a final genre classification experiment. The genre classification experiments were quite successful, yielding a maximum of 91% accuracy using web-based data alone. The effect of weighting schemes and procedural modifications on experimental accuracy rates are discussed, as are qualitative evaluations of participants' responses to the survey