Query by Tapping: A New Paradigm for Content-Based Music Retrieval from Acoustic Input

Jang, Jyh-Shing Roger; Lee, Hong-Ru; Yeh, Chia-Hui

doi:10.1007/3-540-45453-5_76

Cited by 19 publications

(9 citation statements)

References 2 publications

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“…However, note extraction (or segmentation) is error-prone due to the continuity in pitch vectors obtained from human acoustic input. In viewing this disadvantage, the authors have also published their work (Chen & Jang, 1998;Lee, Jang, & Hsu, 1999;Jang, Chen, & Kao, 2001;Jang & Kao, 2000;Jang & Lee, 2001b;Jang, Lee, & Kao, 2001) on improving content-based music retrieval. The focus of Jang and Kao (2000) is on the use of dynamic programming techniques for elastic match in the comparison engine.…”

Section: Classification Of Mir Systemsmentioning

confidence: 94%

“…Jang, Lee, and Kao (2001) proposed a MIRAI system that employs linear scaling for efficient nearest neighbor search. Jang and Lee (2001b) investigates a two-step hierarchical filtering method that can be optimized for MIRAI systems. Jang, Chen, and Kao (2001) explains the operation of a client-server-based MIRAI system that employs cluster computing for efficient retrieval.…”

Section: Classification Of Mir Systemsmentioning

confidence: 99%

“…Since there is no singing or humming, no pitch information is used in the retrieval process. Technical details and per- formance evaluation of query by tapping can be found in Jang and Lee (2001b).…”

Section: Query By Tappingmentioning

confidence: 99%

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Research and developments of a multi‐modal MIR engine for commercial applications in East Asia1

Jang

Lee

Chen

et al. 2004

J. Am. Soc. Inf. Sci.

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This article describes the research and development of an efficient Music Information Retrieval (MIR) engine that is embedded in a karaoke software package targeted for Asian people's need of music retrieval. The MIR engine has a multi-modal interface that allows queries by singing, humming, tapping, speaking, and writing. In particular, we discuss the design philosophy, technical barriers, and performance evaluation of such an engine, as well as its current and potential commercial applications. Feedbacks and feature requests from users, which greatly influence our future work, are also addressed.

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Section: Classification Of Mir Systemsmentioning

confidence: 94%

Section: Classification Of Mir Systemsmentioning

confidence: 99%

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Research and developments of a multi‐modal MIR engine for commercial applications in East Asia1

Jang

Lee

Chen

et al. 2004

J. Am. Soc. Inf. Sci.

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“…The term was introduced in [7] which demonstrated that rhythm alone can be used to retrieve musical works, with their system yielding a top 10 ranking for the desired result 51% of the time. Their work is limited however in considering only monophonic rhythms i.e.…”

Section: Query By Tappingmentioning

confidence: 99%

Finding my beat

Boland

Murray-Smith

2013

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

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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.

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“…Jang et al [19][20] [21][22] [23] start the musical retrieval research earlier, in query by humming, they adopt a classification filter algorithm to removing part of the candidate after the pitch extracted, and accomplished the matching with DTW algorithm. As to query by tapping, melodies are transformed into the time vectors that contain the beat information.…”

Section: Introductionmentioning

confidence: 99%

A novel MIR framework and application with automatic voice processing, database construction and fuzzy matching

Wang

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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In this paper, we present a music information retrieval system which enables users to retrieve music by vocal query. Three essential components are query processing, database construction by MIDI and an approximate search engine. For query processing, we have achieved a real-time and robust voice-to-melody converter. For database construction, proposed MIDI analysis methods to obtain music melody features from MIDI files automatically. In order to match query with melodies in database, we extend an existing search engine into a fast approximate melodic matching engine. We have carried out extensive experiments on the prototype system to evaluate the performance. The results show that the proposed three components are achieving good performance.

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Query by Tapping: A New Paradigm for Content-Based Music Retrieval from Acoustic Input

Cited by 19 publications

References 2 publications

Research and developments of a multi‐modal MIR engine for commercial applications in East Asia1

Research and developments of a multi‐modal MIR engine for commercial applications in East Asia1

Finding my beat

A novel MIR framework and application with automatic voice processing, database construction and fuzzy matching

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