yamnet

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

Raspberry Pi application that detects music with ML, identifies it using Shazam, and shows the song information on an e-ink display

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

Yamnet model using tflite_model_maker with esc-50 dataset

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.

PyTorch implementation of YAMNet model for audio classification

Sound Event Detection with YAMNet on Jetson Nano

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

Software for Hard of Hearing

Python ML for training a custom on-device cry model (knowledge-distilled from YAMNet, INT8, deployed on ESP32-S3)

A Flask web app that fuses classic audio fingerprinting with YAMNet embeddings for lightning-fast, high-accuracy song recognition. Modular code handles spectrogram peak hashing, deep-learning feature extraction, and secure file uploads, all wrapped in a clean UI and built for easy extension or cloud deployment.

Raspberry Pi 5 security system combining real-time YOLOv8 object detection and YAMNet audio classification, with alerts via LED, alarm and email notifications.

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

Local-first toolkit for fixed-camera band rehearsal videos: YAMNet song/highlight/fun-moment detection, multicam-style auto editing on virtual cameras, lossless raw-cut export, and a reusable audio-to-video alignment library for swapping in a clean recorder take of the same performance.

UrbanVibe: SafeRoute - Acoustic-Aware Decision Intelligence Platform & Edge-AI Safeguard for Hearing-Impaired Mobility (MLAI 2026 | TMA Track)

Dog-first local audio event monitor, redesigned from Woofalytics with full credit to original author mdoulaty

Privacy-first baby cry monitor. Runs fully on-device: local cry detection, night logs, and morning digests.

End-to-end AI-powered Infant Cry Diagnostic System using PyTorch, ResNet34, YAMNet, FastAPI, Docker, and AWS S3 with a live inference API.

audio classification flask app using yamnet model.part of safe-journey app