His scientific interests lie mostly in Artificial intelligence, Computer vision, Speech recognition, Motion capture and Audio signal processing. His work carried out in the field of Artificial intelligence brings together such families of science as Music and emotion, Musical development and Pattern recognition. His Computer vision study frequently draws connections between related disciplines such as Dynamic time warping.
The various areas that Meinard Müller examines in his Dynamic time warping study include Segmentation and Information retrieval. His Speech recognition research is multidisciplinary, relying on both Sound recording and reproduction, Timbre, Robustness and Music information retrieval. He works mostly in the field of Motion capture, limiting it down to topics relating to Computer animation and, in certain cases, Data stream mining.
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Music information retrieval, Sound recording and reproduction and Musical. When carried out as part of a general Speech recognition research project, his work on Spectrogram is frequently linked to work in Audio signal processing, therefore connecting diverse disciplines of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Pattern recognition, Computer vision and Natural language processing.
In the subject of general Computer vision, his work in Tracking and Pose is often linked to Noise, thereby combining diverse domains of study. The Music information retrieval study combines topics in areas such as Music theory, Chord and Multimedia. Meinard Müller interconnects Digitization and Pop music automation in the investigation of issues within Multimedia.
Meinard Müller spends much of his time researching Artificial intelligence, Speech recognition, Music information retrieval, Musical and Classical music. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing, Machine learning and Pattern recognition. His Speech recognition study integrates concerns from other disciplines, such as Bass, Convolutional neural network and Beat.
His studies deal with areas such as Melody, Jazz, Popular music and Musicology as well as Music information retrieval. His Musical research incorporates themes from Opera, Singing, Polyphony and World Wide Web. His Computer vision course of study focuses on Piano and Dynamic time warping and MIDI.
His primary areas of investigation include Speech recognition, Music information retrieval, Artificial intelligence, Jazz and Classical music. His research investigates the link between Speech recognition and topics such as Convolutional neural network that cross with problems in Spectrogram. His work on Computational musicology as part of general Music information retrieval research is frequently linked to Digital audio, bridging the gap between disciplines.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition. In his work, Funk, Data-driven and Singing is strongly intertwined with Musical, which is a subfield of Jazz. His Classical music research includes themes of Cognitive science, Style, Composition and Music history.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Information Retrieval for Music and Motion
Dynamic Time Warping
Efficient content-based retrieval of motion capture data
Meinard Müller;Tido Röder;Michael Clausen.
international conference on computer graphics and interactive techniques (2005)
Motion templates for automatic classification and retrieval of motion capture data
Meinard Müller;Tido Röder.
symposium on computer animation (2006)
Documentation Mocap Database HDM05
Meinard Muller;Tido Roder;Michael Clausen;Bernhard Eberhardt.
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera.
Andreas Baak;Meinard Muller;Gaurav Bharaj;Hans-Peter Seidel.
Consumer Depth Cameras for Computer Vision (2013)
State of the Art Report: Audio-Based Music Structure Analysis.
Jouni Paulus;Meinard Müller;Anssi Klapuri.
international symposium/conference on music information retrieval (2010)
Signal Processing for Music Analysis
M. Muller;D. P. W. Ellis;A. Klapuri;G. Richard.
IEEE Journal of Selected Topics in Signal Processing (2011)
Audio Matching via Chroma-Based Statistical Features.
Meinard Müller;Frank Kurth;Michael Clausen.
international symposium/conference on music information retrieval (2005)
Chroma Toolbox: MATLAB Implementations for Extracting Variants of Chroma-based Audio Features
Meinard Müller;Sebastian Ewert.
international symposium/conference on music information retrieval (2011)
Profile was last updated on December 6th, 2021.
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