Query by tapping system based on alignment algorithm

Hanna, Pierre; Robine, Matthias

doi:10.1109/icassp.2009.4959975

Cited by 9 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…providing a fixed reference tone during query recording). Finally, it is probably valid to ask whether we should expect a singing-based retrieval system to work for subjects with poor tuning, who might be directly better off trying search methods which do not involve singing such as query-by-example [34] or queryby-tapping [14].…”

Section: Resultsmentioning

confidence: 99%

Tonal representations for music retrieval: from version identification to query-by-humming

Salamon

Serrà

Gómez

2012

Int J Multimed Info Retr

View full text Add to dashboard Cite

In this study we compare the use of different music representations for retrieving alternative performances of the same musical piece, a task commonly referred to as version identification. Given the audio signal of a song, we compute descriptors representing its melody, bass line and harmonic progression using state-of-the-art algorithms. These descriptors are then employed to retrieve different versions of the same musical piece using a dynamic programming algorithm based on nonlinear time series analysis. First, we evaluate the accuracy obtained using individual descriptors, and then we examine whether performance can be improved by combining these music representations (i.e. descriptor fusion). Our results show that whilst harmony is the most reliable music representation for version identification, the melody and bass line representations also carry useful information for this task. Furthermore, we show that by combining these tonal representations we can increase version detection accuracy. Finally, we demonstrate how the proposed version identification method can be adapted for the task of query-by-humming. We propose a melody-based retrieval approach, and demonstrate how melody representations extracted from recordings of a cappella singing can be successfully used to retrieve the original song from a collection of polyphonic audio. The current limitations of the proposed approach are discussed in the context of version identification and query-by-humming, and possible solutions and future research directions are proposed.

show abstract

Section: Resultsmentioning

confidence: 99%

Tonal representations for music retrieval: from version identification to query-by-humming

Salamon

Serrà

Gómez

2012

Int J Multimed Info Retr

View full text Add to dashboard Cite

show abstract

“…For music signals, a specific line of research was also devoted to more sophisticated user entries such as under the form of a hummed query [103], [104], a sequence of onomatopeia [105] or finger-tapped rhythmic query [106].…”

Section: A Semantic Audio Retrievalmentioning

confidence: 99%

An Overview on Perceptually Motivated Audio Indexing and Classification

Richard

Sundaram²,

Narayanan

2013

Proc. IEEE

View full text Add to dashboard Cite

An audio indexing system aims at describing audio content by identifying, labeling or categorizing different acoustic events. Since the resulting audio classification and indexing is meant for direct human consumption, it is highly desirable that it produces perceptually relevant results. This can be obtained by integrating specific knowledge of the human auditory system in the design process to various extent. In this paper, we highlight some of the important concepts used in audio classification and indexing that are perceptually motivated or that exploit some principles of perception. In particular, we discuss several different strategies to integrate human perception including 1) the use of generic audition models, 2) the use of perceptually-relevant features for the analysis stage that are perceptually justified either as a component of a hearing model or as being correlated with a perceptual dimension of sound similarity, and 3) the involvement of the user in the audio indexing or classification task. In the paper, we also illustrate some of the recent trends in semantic audio retrieval that approximate higher level perceptual processing and cognitive aspects of human audio recognition capabilities including affect-based audio retrieval.

show abstract

“…Our work draws upon both these themes, being the first QBT system to adapt to users. A number of key techniques for QBT are introduced in [6] which describes rhythm as a sequence of time intervals between notes -termed inter-onset intervals (IOIs). They identify the need for such intervals to be defined relative to each other to avoid the user having to exactly recreate the music's tempo.…”

Section: Query By Tappingmentioning

confidence: 99%

“…In previous implementations of QBT, each IOI is defined relative to the preceding one [6]. This sequential dependency compounds user errors in reproducing a rhythm, as an erroneous IOI value will also distort the following one.…”

Section: Query By Tappingmentioning

confidence: 99%

See 1 more Smart Citation

Finding my beat

Boland

Murray-Smith

2013

Proceedings of the 15th International Conference on Human-Computer Interaction With Mobile Devices and Services

View full text Add to dashboard Cite

A novel interaction style is presented, allowing in-pocket music selection by tapping a song's rhythm on a device's touchscreen or body. We introduce the use of rhythmic queries for music retrieval, employing a trained generative model to improve query recognition. We identify rhythm as a fundamental feature of music which can be reproduced easily by listeners, making it an effective and simple interaction technique for retrieving music. We observe that users vary in which instruments they entrain with and our work is the first to model such variability. An experiment was performed, showing that after training the generative model, retrieval performance improved two-fold. All rhythmic queries returned a highly ranked result with the trained generative model, compared with 47% using existing methods. We conclude that generative models of subjective user queries can yield significant performance gains for music retrieval and enable novel interaction techniques such as rhythmic filtering. Human Factors; Design; Performance of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

show abstract

Query by tapping system based on alignment algorithm

Cited by 9 publications

References 6 publications

Tonal representations for music retrieval: from version identification to query-by-humming

Tonal representations for music retrieval: from version identification to query-by-humming

An Overview on Perceptually Motivated Audio Indexing and Classification

Finding my beat

Contact Info

Product

Resources

About