Android Voice Activity Detection (VAD) library. Supports WebRTC VAD GMM, Silero VAD DNN, Yamnet VAD DNN models.

Simple real-time Sound Event Detector based on YAMNet and pyaudio.

Use the yamnet TensorFlow model to classify live audio from a microphone and publish the predicted results to Home Assistant via MQTT

iOS Voice Activity Detection (VAD). Supports WebRTC VAD GMM, Silero VAD DNN, Yamnet VAD DNN models.

Yamnet model using tflite_model_maker with esc-50 dataset

Sound Event Detection with YAMNet+tw on Raspberry Pi

My music tech thesis prototype as well as recent class project

Sound Event Detection with YAMNet on Jetson Nano

YAMNet with latest TensorFlowLiteSwift

Acoustic event detection using yamnet model. Model is deployed using tensorflow serving in docker container and Flask API

Software for Hard of Hearing

Developing a application and/or website for snore detection using DeepLearning

This git repository is an implementation of three different models for accent recognition and evaluation for the language of English

Comprehensive analysis projects featuring image classification using VGG19, LDA-based topic modeling, climate change data visualization with Plotly, and Capuchinbird sound analysis using convolutional neural networks (CNNs) with YAMNet.

This project builds a Music Genre Classification System using SVM, CNN, LSTM, and Transformer models, with YAMNet-based feature extraction. It processes audio files, extracts embeddings, and trains multiple models for comparison. The system predicts genres with high accuracy and provides evaluation metrics like classification reports and confusion.

An ensemble model combining YAMNet and Wav2Vec2 to classify infant cries, screams, and normal utterances with high accuracy. Integrated with Temporal Workflow for real-time inference. 🚀

An AI-powered proctoring tool for secure online exams. Uses YOLOv5 for object detection, MTCNN for face detection, YAMNet for audio detection, and PyQt5 for the UI. Enforces fullscreen mode, disables shortcuts, and provides real-time monitoring with violation tracking. Scalable and resource-efficient.

Exploring audio features for classification and recommendation with the GTZAN using XGBoost, YAMNet, and KNN.

Detects dangerous sounds around you and Alerts you when using sound devices such as headphones

This project aims to build two Machine Learning models for audio recognition, focusing on security and accessibility.