The microseismic signals of coal and rock fractures collected by underground sensors contain masses blasting vibration generated mine blasting, the waveforms two are highly similar. In order to identify true with a monitoring system quickly accurately, this paper proposes lightweight network model that combines convolutional neural (CNN) transformer, named CCViT. Of these, CNN is used extract s...

Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...

If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device like a wheelchair by composing sequencesof these mental states. In this article, we report on a study comparing four representations of EEG signals and their classification by a two-layer neural network with sigmoid activation functions. The neuralnetwork ...

In this contribution, we show that the incorporation of nonlinear dynamical measures into a multivariate discrimination provides a signal classification system that is robust to additive noise. The signal library was composed of nine groups of signals. Four groups were generated computationally from deterministic systems (van der Pol, Lorenz, Rössler and Hénon). Four groups were generated compu...

The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements which show EEG changes at identical scalp ...

Typical signal-based approaches to extract musical descriptions from audio signals only have limited precision. A possible explanation is that they do not exploit take any account of context, which provide important cues in human cognitive processing of music: e.g. electric guitar is unlikely in 1930s music, children choirs rarely perform heavy metal, etc. We propose an architecture to train a ...

Audio signal classification system analyzes the input audio signal and creates a label that describes the signal at the output. These are used to characterize both music and speech signals. The categorization can be done on the basis of pitch, music content, music tempo and rhythm. The signal classifier analyzes the content of the audio format thereby extracting information about the content fr...