What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
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.
His most cited work include:
- Information Retrieval for Music and Motion (724 citations)
- Dynamic Time Warping (416 citations)
- Efficient content-based retrieval of motion capture data (344 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Speech recognition (38.71%)
- Artificial intelligence (30.24%)
- Music information retrieval (20.56%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (30.24%)
- Speech recognition (38.71%)
- Music information retrieval (20.56%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease. (36 citations)
- A Review of Automatic Drum Transcription (23 citations)
- Investigating style evolution of Western classical music: A computational approach: (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
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.
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