A Smartphone-Based Application Using Machine Learning for Gesture Recognition: Using Feature Extraction and Template Matching via Hu Image Moments to Recognize Gestures

Panella, Massimo; Altilio, Rosa

doi:10.1109/mce.2018.2868109

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…Formants are not only determinants of sound quality but also reflect the physical properties of the resonator. e cepstral coefficient can represent the resonance peak, and the definition of the cepstral coefficient including the signal is shown in formula (6):…”

Section: Feature Extraction In Vocalmentioning

confidence: 99%

“…Machine learning can be used for timbre feature extraction in vocal singing, and has a good performance in the extraction accuracy of feature parameters. Panella designed a machine learning algorithm to recognize gestures by using Hu image moments with low computational cost [ 6 ]. Jenke conducted research on emotion recognition by performing feature selection comparative experiments on emotion recorded datasets through machine learning [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Zang

2022

Computational Intelligence and Neuroscience

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With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved.

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Section: Feature Extraction In Vocalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Zang

2022

Computational Intelligence and Neuroscience

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“…However, one practical problem we faced is how to select appropriate CSI subcarriers' DFS change patterns as the input. Since the CSI usually has multiple subcarriers (i.e., 30), and some of them have strong correlations. In other words, they contain redundant information.…”

Section: Basementioning

confidence: 99%

Wi-Learner

Feng

Wang

Jiang

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Contactless RF-based sensing techniques are emerging as a viable means for building gesture recognition systems. While promising, existing RF-based gesture solutions have poor generalization ability when targeting new users, environments or device deployment. They also often require multiple pairs of transceivers and a large number of training samples for each target domain. These limitations either lead to poor cross-domain performance or incur a huge labor cost, hindering their practical adoption. This paper introduces Wi-Learner, a novel RF-based sensing solution that relies on just one pair of transceivers but can deliver accurate cross-domain gesture recognition using just one data sample per gesture for a target user, environment or device setup. Wi-Learner achieves this by first capturing the gesture-induced Doppler frequency shift (DFS) from noisy measurements using carefully designed signal processing schemes. It then employs a convolution neural network-based autoencoder to extract the low-dimensional features to be fed into a downstream model for gesture recognition. Wi-Learner introduces a novel meta-learner to "teach" the neural network to learn effectively from a small set of data points, allowing the base model to quickly adapt to a new domain using just one training sample. By so doing, we reduce the overhead of training data collection and allow a sensing system to adapt to the change of the deployed environment. We evaluate Wi-Learner by applying it to gesture recognition using the Widar 3.0 dataset. Extensive experiments demonstrate Wi-Learner is highly efficient and has a good generalization ability, by delivering an accuracy of 93.2% and 74.2% - 94.9% for in-domain and cross-domain using just one sample per gesture, respectively.

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“…It has been trained using a recurrent neural network (RNN) with an accuracy of up to 93% with three hand gestures [21]. Panella et al [22] discussed the problems in recognizing the hand gestures through hand segmentation in devices like smart phones with less resources. They introduced a new and efficient ML algorithm, which is capable of recognizing the hand gestures through Hu image moments [23].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Sign Language Recognition System for E-Learning Context

Hussain¹,

Shaoor²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

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In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASLalphabet (American Sign Language) and ISL-HS (Irish Sign Language) sign language datasets. Different machine learning classifiers including random forest, decision tree, and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared. Since the random forest classifier performed better, it has been selected as the base classifier for the proposed system. It showed 96.7% accuracy with ISL-HS and 93.7% accuracy with ASL-alphabet dataset using the extracted features.

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A Smartphone-Based Application Using Machine Learning for Gesture Recognition: Using Feature Extraction and Template Matching via Hu Image Moments to Recognize Gestures

Cited by 20 publications

References 14 publications

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning

Wi-Learner

Intelligent Sign Language Recognition System for E-Learning Context

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