curlsloth/Capstone_AcousticEnvironment-DeepNeuralNet
Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.
This project helps environmental researchers, conservationists, or urban planners automatically identify whether an audio recording comes from an urban or natural environment. You input a 10-second audio clip, and the system outputs a classification indicating whether the soundscape is urban or natural. This is designed for professionals who need to categorize large volumes of environmental audio data without manual review.
No commits in the last 6 months.
Use this if you need to automatically sort or analyze audio recordings from soundscapes to determine if they originate from an urban or natural setting.
Not ideal if you require highly precise classification of specific audio events or need to identify detailed sound elements within the recording.
Stars
8
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 25, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/curlsloth/Capstone_AcousticEnvironment-DeepNeuralNet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
aqibsaeed/Urban-Sound-Classification
Urban sound classification using Deep Learning
spotify/realbook
Easier audio-based machine learning with TensorFlow.
ArmDeveloperEcosystem/ml-audio-classifier-example-for-pico
ML Audio Classifier Example for Pico 🔊🔥🔔
IliaZenkov/sklearn-audio-classification
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering,...
mimbres/neural-audio-fp
Official implementation of Neural Audio Fingerprint (ICASSP 2021)