Dear Colleagues,

The problems in signal processing and machine learning fields are similar or related, and the development of modern technology relies on research in these fields. A number of methods and theories have been developed which aim to solve various problems, including in speech and speaker recognition, the classification of signals (image, speech, audio, biomedical signals), emotion recognition and sentiment analysis, signal quality enhancement (filtering and other algorithms), denoising and detection of signals in the presence of noise, and pattern recognition in signals (speech, image, ECG, and other medical signals), with applications in networks, communications, predictive maintenance, as well as business predictions.

One of the goals of signal processing in real time is to reduce the amount of data required to provide a high quality of representation with a reduction in signal. Statistical data processing, statistical signal processing, as well as methods and algorithms which deal with signal reduction all support achievement of this goal.

This Special Issue aims not only to present articles involving the application of methods and algorithms for signal processing and learning but also to promote development in these two fields—both independently and in combination.

This Special Issue will include original research in signal processing, machine learning, and information processing.

Potential topics include, but are not limited to, the following areas of study:

• Parametric estimation in signal and probability density function models of signal source
• Methods in speech recognition and text to speech synthesis
• Speaker identification
• Emotion recognition, sentiment analysis, and face recognition
• Conversational agents (chatbots)
• Natural language processing
• Adaptive signal processing
• Signal processing and learning representation
• Linear and nonlinear regression and data mining
• Machine learning
• Signal extraction and quality enhancement using filtering techniques
• Classification and quantization
• Estimation of statistical parameters in processing of signals
• Deep learning methods
• Quantization in neural networks
• Learning and quantization
• Methods of signal compression and learning
• Autoregressive processing
• Signals, time series, and prediction
• Signal processing and machine learning

Prof. Dr. Zoran H. Perić
Dr. Tomas Skovranek
Prof. Dr. Vlado Delić
Dr. Vladimir Despotovic
Dr. Stefan Panic
Guest Editors

Manuscript Submission Information

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