HMM-based musical query retrieval

Shifrin, Jonah; Pardo, Bryan; Meek, Colin; Birmingham, William P.

doi:10.1145/544290.544291

Cited by 12 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical distances range from a simplified melodic contour to a complex hierarchic description of the melody ( [Ghias et al, 1995], [Sonoda et al, 1998], [Rolland et al, 1999]). Another approach summarizes a whole song by a HMM, and computes the likelihood of the query from this model ( [Shifrin et al, 2002]). Some improvements can be achieved by modelling the common errors that are likely to occur when a user with no musical training is humming a melody ([Jin and Jagadish, 2002]).…”

Section: Scoring and Aligning The Querymentioning

confidence: 99%

Drum Loops Retrieval from Spoken Queries

Gillet

Richard

2005

J Intell Inf Syst

View full text Add to dashboard Cite

Recent efforts in audio indexing and music information retrieval mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembé. In this article, we present a complete system allowing the management of a drum loops database. Queries in this database are formulated with spoken onomatopoeias -short meaningless words imitating the different sounds of the drumkit. The transcription task necessary to index the database is performed using Hidden Markov Models (HMM) and Support Vector Machines (SVM) and achieves a 86.4% correct recognition rate. The syllables of spoken queries are recognized and a relevant statistical model allows the comparison and alignment of the query with the rythmic sequences stored in the database, in order to provide a set of the most relevant drum loops.

show abstract

Section: Scoring and Aligning The Querymentioning

confidence: 99%

Drum Loops Retrieval from Spoken Queries

Gillet

Richard

2005

J Intell Inf Syst

View full text Add to dashboard Cite

show abstract

“…Conversely, ED and k-NN are linear. Another strategy employs HMM-forward algorithms in order to match note transitions and recognize a melody [60].…”

Section: Matching Strategiesmentioning

confidence: 99%

Non-speech voice for sonic interaction: a catalogue

Piccolo

Rocchesso

2016

J Multimodal User Interfaces

View full text Add to dashboard Cite

This paper surveys the uses of non-speech voice as an interaction modality within sonic applications. Three main contexts of use have been identified: sound retrieval, sound synthesis and control, and sound design. An overview of different choices and techniques regarding the style of interaction, the selection of vocal features and their mapping to sound features or controls is here displayed. A comprehensive collection of examples instantiates the use of non-speech voice in actual tools for sonic interaction. It is pointed out that while voice-based techniques are already being used proficiently in sound retrieval and sound synthesis, their use in sound design is still at an exploratory phase. An example of creation of a voice-driven sound design tool is here illustrated.

show abstract

“…A number of works [35,38,42] have utilised Hidden Markov Models (HMMs) in order to represent music pieces in a database and the queries posed. In [35], Pikrakis, et al present a method for automated search of predefined sound patterns within a large number of sound files, using HMMs.…”

Section: Work In Music Databasesmentioning

confidence: 99%

“…In [35], Pikrakis, et al present a method for automated search of predefined sound patterns within a large number of sound files, using HMMs. In [38] the authors use a stochastic representation of both music sequences in the system and the queries, with hidden Markov models in order to handle queries that contain errors or key and tempo changes. Velivelli et al [42] utilise HMMs that can model predefined patterns and simultaneously identify and match an audio segment for a given query.…”

Section: Work In Music Databasesmentioning

confidence: 99%

Finding maximum-length repeating patterns in music databases

Karydis

Νανόπουλος

Manolopoulos

2006

Multimed Tools Appl

View full text Add to dashboard Cite

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an "aggressive" accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

show abstract

HMM-based musical query retrieval

Cited by 12 publications

References 0 publications

Drum Loops Retrieval from Spoken Queries

Drum Loops Retrieval from Spoken Queries

Non-speech voice for sonic interaction: a catalogue

Finding maximum-length repeating patterns in music databases

Contact Info

Product

Resources

About