Medien Wiki - User contributions [en]

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:59:26Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[http://pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Joaquim Jorge'''

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

[[File:Dtw_help.png]]

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

Hopefully with the feedback of the community this external can be released in a nearby future, allowing the DTW algorithm to be easily used from within PD.

[http://vimeo.com/11792446 Early prototype video]

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:56:40Z

PedroLopes:

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:54:34Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[http://pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Joaquim Jorge'''

[[File:Dtw_help.png]]

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

File:Dtw help.png

2011-07-02T17:54:12Z

PedroLopes: Dynamic Time warping external for PD.

== Summary ==
Dynamic Time warping external for PD.
== Copyright status: ==
Pedro Lopes
== Licensing ==
{{self|GFDL|cc-by-sa-all|migration=redundant}}
== Source: ==

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:50:46Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[http://pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Joaquim Jorge'''

[[Image:http://pedrolopesresearch.files.wordpress.com/2011/07/dtw.png]]

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:49:52Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[http://pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Joaquim Jorge'''

[[File:http://pedrolopesresearch.files.wordpress.com/2011/07/dtw.png]]

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:49:36Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[http://pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Jo'aquim Jorge'''

[[File:http://pedrolopesresearch.files.wordpress.com/2011/07/dtw.png]]

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

PDCON:Conference/Dynamic Time Warping for Pure Data

2011-07-02T17:48:37Z

PedroLopes:

== Dynamic time warping for Pure Data ==
Authors: '''[pedrolopesresearch.wordpress.com Pedro Lopes]''' and ''Jo'aquim Jorge'''

[[File:http://pedrolopesresearch.files.wordpress.com/2011/07/dtw.png]]

We present an implementation of the Dynamic Time Warping algorithm for the [[Pure Data]] programming environment. This algorithm is fairly popular in several contexts, ranging from speech processing to pattern detection, mainly because it allows to compare and recognize data sets that may vary non-linearly in time.
Our contribution is easily portable to a wide number of platforms, where Pure Data is available. Throughout this document we describe relevant work that inspired our proposal and present the core concepts of our implementation.

We conclude with an evaluation of our Dynamic Time Warping implementation in two perspectives: performance and adequacy towards gesture recognition. The performance tests suggest that it is suited for realtime contexts, where algorithmic efficiency is of utmost importance. Finally we present a case study where our implementation was used successfully to accommodate gesture recognition on an existing application.

PDCON:Conference

2011-05-21T00:04:11Z

PedroLopes: correcting author names on both my papers

* [[/Custom Spatialisation PD Patch for a Large Non-conventional Area/]] José Rafael Subía Valdez
* [[/Dynamic Time Warping for Pure Data/]] Pedro Lopes and Joaquim Jorge
* [[/Click Tracker: Performance/composition tool for metrically complex scores/]] João Pais and Pedro Lopes
* [[/Dissonance Model Toolbox in Pure Data/]] Alexandre Porres
* [[/Image Processing Algorithm Optimization with CUDA for Pure Data/]] Rudi Giot
* [[/Xth Sense: researching muscle sounds for an experimental paradigm of musical performance/]] Marco Donnarumma
* [[/VisualTracker - modular pd environment for sequencing events on timeline/]] Aleš Černý
* [[/Graphics processing unit audio signals processing in Pure Data, and PDCUDA, an implementation with the CUDA runtime API/]] Charles Henry
* [[/Voice as joystick and oscillator/]] Miller Puckette
* [[/A Framework for Real-time Instrumental Sound Segmentation and Labeling/]] Adriano Monteiro
* [[/Mobile Interfaces with Pure Data/]] Koray Tahiroğlu
* [[/Reverb Design/]] Urban Schlemmer
* [[/Extended View Toolkit/]] Peter Venus
* [[/Electro-Acoustic Tools (EAT): High-Level Abstractions for Audio Manipulation and Spatialisation/]] Richard Thomas
* [[/A Live Performance System in Pure Data: Pitch Contour as Figurative Gesture/]] Richard Graham
* [[/Using Pure Data in Spore and Darkspore - successes and failures/]] Kent Jolly
* [[/Embedding Pure Data with libpd: Design and Workflow/]] Peter Brinkmann
* [[/Pure Data implementation of an ESS-based impulse response acoustic measurement tool/]] Serafino Di Rosario
* [[/AUDIENCE for Pd, a scene-oriented library for spatial audio/]] Regis Faria
* [[/rj - abstractions for getting things done/]] Frank Barknecht
* [[/Plug your cam - extending Gem the modular way/]] IOhannes zmölnig
* [[/Music in expression - A DSP based compositional methodology/]] Chun Lee
* [[/DILIB: Control Data Parsing for Digital Musical Instrument Design/]] William Brent
* [[/A collaboration workflow from sound-based composition to performance of electroacoustic music using Pure Data as a framework/]] Kyriakos Tsoukalas
* [[/Using Pd to create a visualization instrument for percussion/]] John Harrison
* [[/Gemnotes: A Realtime music notation system for pure data/]] Edward Kelly
* [[/Self-Modifying Help Patches/]] Mathieu Bouchard
* [[/Patching for multi-instantiation: why don't put more inside?/]] Krzysztof Czaja
* [[/GUI Plugins in Pd 0.43/]] Hans-Christoph Steiner
* [[/Santiago - Making music with biological neural networks in Pd-Gem/]] Hernán Kerlleñevich