What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
His primary areas of investigation include Microphone array, Algorithm, Speech recognition, Microphone and Estimator.
His Microphone array research focuses on subjects like Maxima and minima, which are linked to Stochastic process and Computation.
His study in the field of Algorithm design is also linked to topics like Restricted range.
His Speech recognition study which covers Reverberation that intersects with Speech processing and Robustness.
The various areas that Harvey F. Silverman examines in his Microphone study include Two stage algorithm, Sensor array, Maximization and Signal processing.
His Estimator study incorporates themes from Range, Ambient noise level, Transducer and Teleconference.
His most cited work include:
- A Class of Algorithms for Fast Digital Image Registration (894 citations)
- Robust Localization in Reverberant Rooms (480 citations)
- Processor reconfiguration through instruction-set metamorphosis (348 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Speech recognition, Microphone array, Microphone, Artificial intelligence and Algorithm.
His research on Speech recognition often connects related topics like Signal processing.
His work deals with themes such as Digital signal processing, Computer engineering, Reverberation and Latency, which intersect with Microphone array.
His Microphone research incorporates elements of Acoustics, Noise, Beamforming and Sensor array.
His work deals with themes such as Natural language processing, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His work carried out in the field of Algorithm brings together such families of science as Discrete Fourier transform, Fractional Fourier transform, Discrete-time Fourier transform and Estimator.
He most often published in these fields:
- Speech recognition (50.68%)
- Microphone array (30.41%)
- Microphone (25.68%)
What were the highlights of his more recent work (between 2004-2018)?
- Speech recognition (50.68%)
- Microphone array (30.41%)
- Algorithm (22.97%)
In recent papers he was focusing on the following fields of study:
Harvey F. Silverman spends much of his time researching Speech recognition, Microphone array, Algorithm, Microphone and Speech processing.
He studies Speech recognition, focusing on Spectrogram in particular.
His biological study spans a wide range of topics, including Acoustics, Artificial intelligence and Computer vision.
His research integrates issues of Signal-to-noise ratio and Pattern recognition in his study of Artificial intelligence.
His study in the fields of Algorithm design under the domain of Algorithm overlaps with other disciplines such as Power.
Harvey F. Silverman has researched Microphone in several fields, including Beamforming and Array processing.
Between 2004 and 2018, his most popular works were:
- A Real-Time SRP-PHAT Source Location Implementation using Stochastic Region Contraction(SRC) on a Large-Aperture Microphone Array (182 citations)
- A Linear Closed-Form Algorithm for Source Localization From Time-Differences of Arrival (141 citations)
- Performance of real-time source-location estimators for a large-aperture microphone array (78 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary areas of study are Microphone array, Algorithm, Microphone, Speech recognition and Maxima and minima.
His studies in Microphone array integrate themes in fields like Acoustics and Speech processing.
His Algorithm studies intersect with other disciplines such as Invertible matrix and Hyperboloid.
The concepts of his Microphone study are interwoven with issues in Algorithm design, Noise and Array processing.
His Speech recognition research is multidisciplinary, incorporating perspectives in Signal-to-noise ratio and Mixture model, Artificial intelligence, Pattern recognition.
His study looks at the intersection of Maxima and minima and topics like Stochastic process with Computation and Coarse to fine.
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.
