We study the denoising and reconstruction of corrupted signals by means of AutoEncoder ensembles. In order to guarantee experts' diversity in the ensemble, we apply, prior to learning, a dimensional reduction pass (to map the examples into a suitable Euclidean space) and a partitional clustering pass: each cluster is then used to train a distinct AutoEncoder. We study the approach with an audio file benchmark: the original signals are artificially corrupted by Doppler effect and reverb. The results support the comparative effectiveness of the approach, w.r.t. the approach based on a single AutoEncoder. The processing pipeline using Local Linear Embedding, k means, then k Convolutional Denoising AutoEncoders reduces the reconstruction error by 35% w.r.t. the baseline approach.

Mio, C., Gianini, G. (2019). Signal reconstruction by means of Embedding, Clustering and AutoEncoder Ensembles. In Proceedings - IEEE Symposium on Computers and Communications (pp.1-6). IEEE [10.1109/ISCC47284.2019.8969655].

Signal reconstruction by means of Embedding, Clustering and AutoEncoder Ensembles

Gianini, G
2019

Abstract

We study the denoising and reconstruction of corrupted signals by means of AutoEncoder ensembles. In order to guarantee experts' diversity in the ensemble, we apply, prior to learning, a dimensional reduction pass (to map the examples into a suitable Euclidean space) and a partitional clustering pass: each cluster is then used to train a distinct AutoEncoder. We study the approach with an audio file benchmark: the original signals are artificially corrupted by Doppler effect and reverb. The results support the comparative effectiveness of the approach, w.r.t. the approach based on a single AutoEncoder. The processing pipeline using Local Linear Embedding, k means, then k Convolutional Denoising AutoEncoders reduces the reconstruction error by 35% w.r.t. the baseline approach.
paper
Embeddings; Learning systems
English
2019 IEEE Symposium on Computers and Communications, ISCC 2019 - 29 June 2019 through 3 July 2019
2019
Proceedings - IEEE Symposium on Computers and Communications
9781728129990
2019
2019-June
1
6
8969655
reserved
Mio, C., Gianini, G. (2019). Signal reconstruction by means of Embedding, Clustering and AutoEncoder Ensembles. In Proceedings - IEEE Symposium on Computers and Communications (pp.1-6). IEEE [10.1109/ISCC47284.2019.8969655].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454847
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