EARS: Environmental Audio Recognition System
-
Updated
Apr 4, 2018 - Python
EARS: Environmental Audio Recognition System
Classify audio with neural nets on embedded systems like the Raspberry Pi
ConvNets for Audio Recognition using Google Commands Dataset
Implementation of "Slow-Fast Auditory Streams for Audio Recognition, ICASSP, 2021" in PyTorch
SELD-TCN: Sound Event Detection & Localization via Temporal Convolutional Network | Python w/ Tensorflow
Source code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.
General purpose, real-time audio recognition engine
Google Speech Command Dataset Classification Neural Network, CNN, RNN
Flutter mobile application for audio recognition using Tensorflow Lite to integrate the classification model
Redis Audio Track Recognition
📻💡 Recognize audio recordings with node and the acr-cloud recognition API
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and con…
Audio classification using Keras with ESC-50 dataset.
Detecting lip popping noises to trigger an action.
基于SSM框架的听歌识曲系统
This code was made in an effort to make it easier to find the song that has been dubbed as 'the most mysterious song on the internet' and it makes it possible to search youtube channels for songs without having to manually check the videos.
Minimalist Speech-to-Text toolkit for educational purposes
Categorize audio files by genre effortlessly. Use Dockerized environment and API to classify music genres.
Vanilla Music player file Metadata Fetch plugin
Add a description, image, and links to the audio-recognition topic page so that developers can more easily learn about it.
To associate your repository with the audio-recognition topic, visit your repo's landing page and select "manage topics."