Uniform identity authentication system based on ESB in heterogeneous environment

Li, Fulin; Xu, Kaiyong; Fan, Xiaolan; Li, Lixin

doi:10.1109/iceceng.2011.6057975

Cited by 2 publications

(2 citation statements)

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“…Figure 2 shows the spectrogram of the examples of "Good", "Fair", or "Poor" piano performances for playing the line "Lay thee down now and rest, may thy slumber be blessed" in Brahms' Lullaby. The x-axis and y-axis of a spectrogram represent time and frequency [16], respectively. The brightness of the spectrogram represents amplitude, where darker pixels indicate smaller values and brighter pixels indicate larger values.…”

Section: Methodsmentioning

confidence: 99%

Automatic Assessment of Piano Performances Using Timbre and Pitch Features

Phanichraksaphong

Tsai

2023

Electronics

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To assist piano learners with the improvement of their skills, this study investigates techniques for automatically assessing piano performances based on timbre and pitch features. The assessment is formulated as a classification problem that classifies piano performances as “Good”, “Fair”, or “Poor”. For timbre-based approaches, we propose timbre-based WaveNet, timbre-based MLNet, Timbre-based CNN, and Timbre-based CNN Transformers. For pitch-based approaches, we propose Pitch-based CNN and Pitch-based CNN Transformers. Our experiments indicate that both Pitch-based CNN and Pitch-based CNN Transformers are superior to the timbre-based approaches, which attained classification accuracies of 96.87% and 97.5%, respectively.

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Section: Methodsmentioning

confidence: 99%

Automatic Assessment of Piano Performances Using Timbre and Pitch Features

Phanichraksaphong

Tsai

2023

Electronics

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“…Calculating the logarithmic value can be achieved using the Librosa library. Librosa, a Python package tailored for music and audio analysis, encompasses essential components to construct information retrieval systems employed in audio analysis[54][55][56].Utilizing spectrograms, which visually represent sound signals, and employing deep learning for classification purposes is crucial due to several reasons. Firstly, spectrograms enable the capture of complex patterns within audio data, facilitating comprehensive analysis.…”

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confidence: 99%

Vision Transformer Based Classification of Neurological Disorders from Human Speech

Soylu,

Gül,

Aslan

et al. 2024

Firat University Journal of Experimental and Computational Engineering

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In this study, we introduce a transformative approach to achieve high-accuracy classification of distinct health categories, including Parkinson's disease, Multiple Sclerosis (MS), healthy individuals, and other categories, utilizing a transformer-based neural network. The cornerstone of this approach lies in the innovative conversion of human speech into spectrograms, which are subsequently transformed into visual images. This transformation process enables our network to capture intricate vocal patterns and subtle nuances that are indicative of various health conditions. The experimental validation of our approach underscores its remarkable performance, achieving exceptional accuracy in differentiating Parkinson's disease, MS, healthy subjects, and other categories. This breakthrough opens doors to potential clinical applications, offering an innovative, non-invasive diagnostic tool that rests on the fusion of spectrogram analysis and transformer-based models.

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Uniform identity authentication system based on ESB in heterogeneous environment

Cited by 2 publications

References 0 publications

Automatic Assessment of Piano Performances Using Timbre and Pitch Features

Automatic Assessment of Piano Performances Using Timbre and Pitch Features

Vision Transformer Based Classification of Neurological Disorders from Human Speech

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