A Document-Centered Approach to a Natural Language Music Search Engine

Knees, Peter; Pohle, Tim; Schedl, Markus; Schnitzer, Dominik; Seyerlehner, Klaus

doi:10.1007/978-3-540-78646-7_68

Cited by 3 publications

(2 citation statements)

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“…One can easily gather millions of tags, but the main drawback of this method is the noise in the data. Other approaches include querying search engines (Knees et al, 2008) and mining music websites (Whitman & Ellis, 2004).…”

Section: Sources Of Datamentioning

confidence: 99%

Automatic Tagging of Audio

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Recently there has been a great deal of attention paid to the automatic prediction of tags for music and audio in general. Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of ``Web 2.0‘‘ recommender systems. There have been many attempts at automatically applying tags to audio for different purposes: database management, music recommendation, improved human-computer interfaces, estimating similarity among songs, and so on. Many published results show that this problem can be tackled using machine learning techniques, however, no method so far has been proven to be particularly suited to the task. First, it seems that no one has yet found an appropriate algorithm to solve this challenge. But second, the task definition itself is problematic. In an effort to better understand the task and also to help new researchers bring their insights to bear on this problem, this chapter provides a review of the state-of-the-art methods for addressing automatic tagging of audio. It is divided in the following sections: goal, framework, audio representation, labeled data, classification, evaluation, and future directions. Such a division helps understand the commonalities and strengths of the different methods that have been proposed.

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Section: Sources Of Datamentioning

confidence: 99%

Automatic Tagging of Audio

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“…is performed based on acoustic features of the singer voice. Sometimes, social context data such as web data can be used for this purpose [69]. Table 3 lists the common data mining and machine learning techniques employed in the automatic artist identification/classification literature.…”

Section: Music Retrieval Tasks and Common Techniquesmentioning

confidence: 99%

User-centric Music Information Retrieval

Shao

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The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users' navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users' satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, useraccess patterns, and user-defined tags, and on targeting non-genre categories for classification, v such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns. vi

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