On time-frequency correlation in spectrogram samples with application to target detection

Parisi, Gianluca; Coluccia, Angelo; Fascista, Alessio

doi:10.1016/j.sigpro.2022.108648

Cited by 2 publications

(1 citation statement)

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“…In this study, feautured engineering for neural net has been employed. Feautured data using multiple sources of datasets, and preprocessing by transforming voice signals in the time domain into spectrogram images as in this paper [56][57][58][59][60][61]. Deep learning using lightweight architecture CNN-STFT with 2 convolution layer, and adam optimizer [62][63][64][65][66][67][68][69][70].…”

Section: Tinyml Controller Designmentioning

confidence: 99%

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

Riyanta,

Irianta,

Kamiel

2023

Journal of Robotics and Control

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Post-stroke dysarthria (PSD) is a widespread outcome of a stroke. To help in the objective evaluation of dysarthria, the development of pathological voice recognition and technology has a lot of attention. Soft robotics therapy devices have been received as an alternative rehabilitation and hand grasp assistance for improving activity daily living (ADL). Despite the significant progress in this field, most soft robotic therapy devices use a complex, bulky, lack of pathological voice recognition model, large computational power, and stationary controller. This study aims to develop a portable wirelessly multi-controller with a simulated dysarthric vowel speech in Bahasa Indonesia and non-dysarthric micro speech recognition, using tiny machine learning (TinyMl) system for hardware efficiency. The speech interface using INMP441, compute with a lightweight Deep Convolutional Neural network (DCNN) design and embedded into ESP-32. Feature model using Short Time Fourier Transform (STFT) and fed into CNN. This method has proven useful in micro-speech recognition with low computational power in both speech scenarios with a level of accuracy above 90%. Realtime inference performance on ESP-32 using hand prosthetics, with 3-level household noise intensity respectively 24db,42db, and 62db, and has respectively resulted from 95%, 85%, and 50% Accuracy. Wireless connectivity success rate with both controllers is around 0.2 - 0.5 ms.

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