What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Speech recognition, Artificial intelligence, Pattern recognition, Multimedia and Speech processing.
His work carried out in the field of Speech recognition brings together such families of science as Representation, Sound, Noise, Audio signal processing and Feature extraction.
His Artificial intelligence study combines topics in areas such as Computer vision and Dynamics.
His Hidden Markov model study, which is part of a larger body of work in Pattern recognition, is frequently linked to Path, bridging the gap between disciplines.
Malcolm Slaney has researched Hidden Markov model in several fields, including Time delay neural network and Convolutional neural network.
His work on Music and artificial intelligence as part of general Multimedia study is frequently connected to Pop music automation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His most cited work include:
- Principles of Computerized Tomographic Imaging (4530 citations)
- Construction and evaluation of a robust multifeature speech/music discriminator (846 citations)
- CNN architectures for large-scale audio classification (635 citations)
What are the main themes of his work throughout his whole career to date?
Malcolm Slaney mostly deals with Speech recognition, Artificial intelligence, Multimedia, Pattern recognition and Information retrieval.
As part of one scientific family, Malcolm Slaney deals mainly with the area of Speech recognition, narrowing it down to issues related to the Mel-frequency cepstrum, and often Timbre.
His biological study spans a wide range of topics, including Machine learning, Computer vision and Natural language processing.
Malcolm Slaney is interested in Image processing, which is a field of Computer vision.
His work is dedicated to discovering how Multimedia, World Wide Web are connected with Content and other disciplines.
His Information retrieval research is multidisciplinary, relying on both Probabilistic latent semantic analysis, Image and Scale space.
He most often published in these fields:
- Speech recognition (32.84%)
- Artificial intelligence (25.49%)
- Multimedia (17.65%)
What were the highlights of his more recent work (between 2012-2021)?
- Speech recognition (32.84%)
- Artificial intelligence (25.49%)
- Decoding methods (3.43%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Decoding methods, Eye tracking and Artificial neural network.
Malcolm Slaney interconnects Beamforming, Regression and Gaze in the investigation of issues within Speech recognition.
His studies in Artificial intelligence integrate themes in fields like Natural language processing and Pattern recognition.
Malcolm Slaney combines subjects such as Speech sound, Auditory perception, Canonical correlation and Set with his study of Decoding methods.
His study in the fields of Time delay neural network under the domain of Artificial neural network overlaps with other disciplines such as Bottleneck.
His study in Object is interdisciplinary in nature, drawing from both Sequence, Multimedia and Mood.
Between 2012 and 2021, his most popular works were:
- CNN architectures for large-scale audio classification (635 citations)
- Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG (385 citations)
- An Introduction to Computational Networks and the Computational Network Toolkit (318 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Speech recognition, Artificial intelligence, Training set, Pattern recognition and Convolutional neural network.
His Speech recognition research is multidisciplinary, incorporating perspectives in Decoding methods and Tone.
His Taxonomy study in the realm of Artificial intelligence connects with subjects such as Distortion.
His work deals with themes such as Machine learning, Feature, Classifier, Dictation and Voice search, which intersect with Training set.
In his study, Sound recording and reproduction, Mel-frequency cepstrum and Vocal tract is inextricably linked to Artificial neural network, which falls within the broad field of Pattern recognition.
His research investigates the connection between Convolutional neural network and topics such as Contextual image classification that intersect with issues in Spectrogram.
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