The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies. After obtaining two suitable datasets, we proceeded to classify the sounds using two learnable state-of-art frontends – LEAF and nnAudio – plus a non-learnable baseline frontend, i.e. Mel-filterbanks. The computed features are then fed into two different CNN models, namely VGG16 and EfficientNet. The frontends are carefully benchmarked in terms of the number of parameters, computational resources, and effectiveness. This work demonstrates how the integration of learnable frontends in neuralaudio classification systems may improve performance, especially in the field of medical acoustics. However, the usage of such frameworks makes the needed amount of data even larger. Consequently, they are useful if the amount of data available for training is adequately large to assist the feature learning process.
Deep Feature Learning for Medical Acoustics / A.M. Poire, F. Simonetta, S. Ntalampiras (LECTURE NOTES IN COMPUTER SCIENCE). - In: ICANN 2022: Artificial Neural Networks and Machine Learning / [a cura di] E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, M. Aydin. - Prima edizione. - [s.l] : Springer, 2022. - ISBN 978-3-031-15936-7. - pp. 39-50 (( Intervento presentato al 31. convegno International Conference on Artificial Neural Networks : September, 6th - 9th tenutosi a Bristol, nel 2022 [10.1007/978-3-031-15937-4_4].
Deep Feature Learning for Medical Acoustics
F. Simonetta;S. Ntalampiras
2022
Abstract
The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies. After obtaining two suitable datasets, we proceeded to classify the sounds using two learnable state-of-art frontends – LEAF and nnAudio – plus a non-learnable baseline frontend, i.e. Mel-filterbanks. The computed features are then fed into two different CNN models, namely VGG16 and EfficientNet. The frontends are carefully benchmarked in terms of the number of parameters, computational resources, and effectiveness. This work demonstrates how the integration of learnable frontends in neuralaudio classification systems may improve performance, especially in the field of medical acoustics. However, the usage of such frameworks makes the needed amount of data even larger. Consequently, they are useful if the amount of data available for training is adequately large to assist the feature learning process.File | Dimensione | Formato | |
---|---|---|---|
978-3-031-15937-4_4.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
650.59 kB
Formato
Adobe PDF
|
650.59 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
2208.03084.pdf
Open Access dal 08/09/2023
Tipologia:
Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione
727.59 kB
Formato
Adobe PDF
|
727.59 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.