ArticleSensors (Basel, Switzerland)2021
A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Firearm classification from acoustic signals using combined mel spectrogram, MFCC, LFCC, and CRNN networks.Scientific reports · 2025Article
- Real-Time Acoustic Scene Recognition for Elderly Daily Routines Using Edge-Based Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization.Frontiers in big data · 2024Article
- BSN-ESC: A Big-Small Network-Based Environmental Sound Classification Method for AIoT Applications.Sensors (Basel, Switzerland) · 2023Article
- Analysis and Acoustic Event Classification of Environmental Data Collected in a Citizen Science Project.International journal of environmental research and public health · 2023Article
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning Smart System for Parkinson Disease Classification Using the Voice as a Biomarker.Healthcare informatics research · 2022Article
- Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis.Sensors (Basel, Switzerland) · 2021Article
- Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data.International journal of environmental research and public health · 2021Article
- Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (
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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.