ArticleSensors (Basel, Switzerland)2020
Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models.Sensors (Basel, Switzerland) · 2022Article
- Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models.Sensors (Basel, Switzerland) · 2021Article
- Detection Line Spectrum of Ship Radiated Noise Based on a New 3D Chaotic System.Sensors (Basel, Switzerland) · 2021Article
- Sensor Signal and Information Processing III.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.