Music Information Retrieval: Recent Developments and Applications

Schedl, Markus; Gómez, Emilia; Urbano, Julián

doi:10.1561/1500000042

Cited by 171 publications

(122 citation statements)

References 154 publications

(30 reference statements)

Supporting

Mentioning

118

Contrasting

Unclassified

Order By: Relevance

“…Multipitch (multiple f 0 ) estimation can be considered as one of the main challenges in the MIR field, as they need to deal with masking, overlapping tones, mixture of harmonic and non-harmonic sources and the fact that the number of sources might be unknown (Schedl, Gómez, and Urbano 2014). Given the complexity of the musical material in consideration, it would be virtually impossible with current methods to estimate and track all present pitches.…”

Section: Evaluated Approachesmentioning

confidence: 99%

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Bosch

Marxer

2016

Journal of New Music Research

Self Cite

View full text Add to dashboard Cite

The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterised by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signals, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results.

show abstract

Section: Evaluated Approachesmentioning

confidence: 99%

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Bosch

Marxer

2016

Journal of New Music Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accordingly, they recommend to increase the visibility and impact of user-based studies in the field. A very comprehensive review of topics related to MIR was recently prepared by Schedl et al (2014). The study contains over 300 references, however this is only a small fraction of the literature sources devoted to Music Information Retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic music genre classification (AMGC) has been exploited quite thoroughly in recent years by the research community (ISMIR conferences, ISMIR (2016)) and is one of the most popular search query choices within the MIR domain (Bergstra et al 2006;Burred 2014;Kostek 2005;Ntalampiras 2013;Schedl et al 2014;Silla et al 2007;Sturm 2013;Tzanetakis et al 2002)). On a smaller scale, a survey, focusing on AMGC was presented by Silla et al (2007).…”

Section: Introductionmentioning

confidence: 99%

“…One may argue that the expanding consumer market for social music network services brought new ways for searching and analyzing musical information and examining their effectiveness and quality, i.e. based on collaborative filtering and similarity measures retrieved from large music archives (Schedl et al 2014;Ness et al 2009). However, the subject of a deeper content exploring, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic music genre classification based on musical instrument track separation

Rosner

Kostek

2017

J Intell Inf Syst

View full text Add to dashboard Cite

The aim of this article is to investigate whether separating music tracks at the preprocessing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach proposed for music genre classification is promising. Overall, conglomerating parameters derived from both an original audio and a mixture of separated tracks improve classification effectiveness measures, demonstrating that the proposed feature vector and the Support Vector Machine (SVM) with Co-training mechanism are applicable to a large dataset.

show abstract

“…[4][5][6]. In those systems, some musical-acoustical features are primitively extracted from a music dataset and stored.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Query-by-Example based Music Information Retrieval

Borjian¹

2017

IJCA

View full text Add to dashboard Cite

ABSTRACTsearch in huge musical datasets using a query provided as a fragment of desired song while there exists no extra information is a particular concern in content-based music information retrieval (MIR), defined as query-by-example (QBE). A number of QBE based MIR systems have evolved in recent years, which search a desired song without any manual of its originality, such as title, composer, singer or etc., and return a list of songs ranked in descending order according to the similarity with the given query recorded by user on TV, in gym or so on. Although, too much attention has been paid to this topic by researchers and developers in several communities, such as information retrieval, data mining or multimedia browsing engines, but it still suffers from no existing a unique definition on structure, aim, similarity, performance and also output results. This paper focuses on providing a brief overview of available QBE based MIR systems to manifest variety, opportunities and challenges in this area.

show abstract

Music Information Retrieval: Recent Developments and Applications

Cited by 171 publications

References 154 publications

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Automatic music genre classification based on musical instrument track separation

A Survey on Query-by-Example based Music Information Retrieval

Contact Info

Product

Resources

About