What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Simon Dixon mainly focuses on Speech recognition, Artificial intelligence, Algorithm, Music information retrieval and Rhythm.
Simon Dixon has researched Speech recognition in several fields, including Chord, Transcription, Musical, MIDI and Beat.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His work in the fields of Algorithm, such as Source separation, overlaps with other areas such as Frame.
The Music information retrieval study combines topics in areas such as Multimedia, Transcription, Pop music automation, Data science and Audio signal.
His Rhythm study integrates concerns from other disciplines, such as Timbre, World Wide Web, Audio analyzer and Metaphor.
His most cited work include:
- Automatic Extraction of Tempo and Beat From Expressive Performances (324 citations)
- Automatic music transcription: challenges and future directions (192 citations)
- An experimental comparison of audio tempo induction algorithms (185 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Speech recognition, Artificial intelligence, Pattern recognition, Musical and Music information retrieval.
His research in Speech recognition intersects with topics in Transcription, Beat, Rhythm and Piano.
Simon Dixon has included themes like Time signature and Beat detection in his Beat study.
His work carried out in the field of Artificial intelligence brings together such families of science as Chord, Machine learning and Natural language processing.
His work in Pattern recognition covers topics such as Source separation which are related to areas like Adversarial system and Artificial neural network.
His Music information retrieval research is multidisciplinary, incorporating elements of Multimedia and Data science.
He most often published in these fields:
- Speech recognition (46.22%)
- Artificial intelligence (36.89%)
- Pattern recognition (17.33%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (36.89%)
- Pattern recognition (17.33%)
- Speech recognition (46.22%)
In recent papers he was focusing on the following fields of study:
Simon Dixon spends much of his time researching Artificial intelligence, Pattern recognition, Speech recognition, Source separation and Singing.
His Artificial intelligence research is multidisciplinary, relying on both Context and Machine learning.
His Context research incorporates themes from Music information retrieval and Identification.
His study on Speech recognition also encompasses disciplines like
- Lyrics that intertwine with fields like Language model,
- Duration together with Audio signal, Word and Sound recording and reproduction.
His Source separation research includes themes of Adversarial system, Image segmentation, Leverage, Inpainting and Deep learning.
His Singing research focuses on Active listening and how it connects with Bass, Pitch variation, Intonation, Fundamental frequency and SATB.
Between 2018 and 2021, his most popular works were:
- Automatic Music Transcription: An Overview (39 citations)
- Investigating style evolution of Western classical music: A computational approach: (15 citations)
- A Hybrid Approach to Audio-to-Score Alignment. (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Artificial intelligence, Singing, Pattern recognition, Speech recognition and Transcription are his primary areas of study.
His research brings together the fields of Machine learning and Artificial intelligence.
His Singing research includes elements of Bass, Pitch error, Active listening, Absolute pitch and Pitch variation.
He interconnects Artificial neural network, Deep learning, Automatic learning and Kernel in the investigation of issues within Pattern recognition.
His study in Speech recognition is interdisciplinary in nature, drawing from both Inference, Melody, Musical, Task analysis and Human intelligence.
His Transcription study incorporates themes from Pronunciation, Language model, Musical notation, Knowledge representation and reasoning and Lyrics.
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.
