Recent studies on music information processing

Goto, Masataka; Hirata, Keiji

doi:10.1250/ast.25.419

Cited by 17 publications

(16 citation statements)

References 47 publications

(52 reference statements)

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“…A central goal of the music information retrieval community is to create systems that efficiently store and retrieve songs from large databases of musical content [3]. The most common way to store and retrieve music uses metadata such as the name of the composer or artist, the name of the song, or the release date of the album.…”

Section: Related Workmentioning

confidence: 99%

“…Another retrieval approach, called query-by-similarity, takes an audio-based query and measures the similarity between the query and all of the songs in a database [3]. A limitation of query-by-similarity is that it requires a user to have a useful audio exemplar in order to specify a query.…”

Section: Related Workmentioning

confidence: 99%

“…Given four test subjects, two subjects positively associate the song with the word because the overall guitar sound is clean, one is unsure, and one says there is no "clean electric guitar" presumably because, technically, the guitarist makes use of a delay distortion. 3 Our summarization process would not use the word to label this songs despite the fact that half of the subjects used this word to describe the song. In Section VIII, we will discuss both ways to improve the survey process as well as an alternative data collection technique.…”

Section: A Annotationmentioning

confidence: 99%

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Semantic Annotation and Retrieval of Music and Sound Effects

Turnbull

Barrington

Torres

et al. 2008

IEEE Trans. Audio Speech Lang. Process.

366

254

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Abstract-We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our "query-by-text" system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.Index Terms-Audio annotation and retrieval, music information retrieval, semantic music analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Annotationmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Annotation and Retrieval of Music and Sound Effects

Turnbull

Barrington

Torres

et al. 2008

IEEE Trans. Audio Speech Lang. Process.

366

254

View full text Add to dashboard Cite

show abstract

“…Although tag-based search querying is arguably one of the most intuitive methods for retrieving music, until very recently [2,10,24], most retrieval methods have focused on querying metadata such as artist or album title [28], similarity to an audio input query [6,7,8], or a small fixed set of category labels based on genre [26], mood [23], or instrument [9]. The lack of focus on music retrieval by rich and diverse semantic tags is partly due to a historical lack of labeled data for training music tagging systems.…”

Section: Introductionmentioning

confidence: 99%

Learning to Tag from Open Vocabulary Labels

Law

Settles

Mitchell

2010

Machine Learning and Knowledge Discovery in Databases

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Abstract. Most approaches to classifying media content assume a fixed, closed vocabulary of labels. In contrast, we advocate machine learning approaches which take advantage of the millions of free-form tags obtainable via online crowd-sourcing platforms and social tagging websites. The use of such open vocabularies presents learning challenges due to typographical errors, synonymy, and a potentially unbounded set of tag labels. In this work, we present a new approach that organizes these noisy tags into well-behaved semantic classes using topic modeling, and learn to predict tags accurately using a mixture of topic classes. This method can utilize an arbitrary open vocabulary of tags, reduces training time by 94% compared to learning from these tags directly, and achieves comparable performance for classification and superior performance for retrieval. We also demonstrate that on open vocabulary tasks, human evaluations are essential for measuring the true performance of tag classifiers, which traditional evaluation methods will consistently underestimate. We focus on the domain of tagging music clips, and demonstrate our results using data collected with a human computation game called TagATune.

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“…As research on music information processing [1,2,3], including research on music information retrieval [4], has continued to rapidly expand, research activities related to singing have also become more vigorous. Such activities are attracting attention not only from a scientific point of view, but also from the standpoint of industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Singing information processing based on singing voice modeling

Goto

Saitou

Nakano

et al. 2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we propose a novel area of research referred to as singing information processing. To shape the concept of this area, we first introduce singing understanding systems for synchronizing between vocal melody and corresponding lyrics, identifying the singer name, evaluating singing skills, creating hyperlinks between phrases in the lyrics of songs, and detecting breath sounds. We then introduce music information retrieval systems based on similarity of vocal melody timbre and vocal percussion, and singing synthesis systems. Common signal processing techniques for modeling singing voices that are used in these systems, such as techniques for extracting the vocal melody from polyphonic music recordings and modeling the lyrics by using phoneme HMMs for singing voices, are discussed.Index Terms-Music, singing information processing, singing voice modeling, vocal melody INTRODUCTIONAs research on music information processing [1, 2, 3], including research on music information retrieval [4], has continued to rapidly expand, research activities related to singing have also become more vigorous. Such activities are attracting attention not only from a scientific point of view, but also from the standpoint of industrial applications. Singing-related research is highly diverse, ranging from basic research on the features unique to singing to applied research such as that on the synthesis of singing voices, lyrics recognition, singer identification, retrieval of singing voices, and singing-skill evaluation. In this paper, we refer to this broad range of singing-related studies as singing information processing and introduce examples of these studies with the focus on signal processing techniques for modeling singing voices.Singing possesses aspects of both speech and music, and there are many unsolved research problems from the viewpoint of either field. For example, singing voices generally fluctuate more than speaking voices, and musical accompaniment, which is closely interlinked with singing, is usually included at a relatively high volume. Because of these characteristics, the automatic recognition of singing is the most difficult class of speech recognition from a technical point of view. In fact, the automatic recognition of lyrics in vocals has not yet been fully achieved. Furthermore, from the viewpoint of music recognition and understanding, large fluctuations and variations in singing cause various difficulties compared to a similar analysis of musical instruments. Technically speaking, there are many difficult and deeply interesting problems in this regard. Similarly, in the research on singing synthesis, many problems still exist, since, in addition to conveying content in the form of language as in speaking, singing synthesis requires dynamic, complex, and expressive changes in the voice pitch, intensity, and timbre of singing. In this way, the study of singing information processing is a genuine frontier of science.Moreover, while music is an important type of content from the viewpoints o...

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Recent studies on music information processing

Cited by 17 publications

References 47 publications

Semantic Annotation and Retrieval of Music and Sound Effects

Semantic Annotation and Retrieval of Music and Sound Effects

Learning to Tag from Open Vocabulary Labels

Singing information processing based on singing voice modeling

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