SAISE_Dataset

This dataset is part of the article titled "A tinyML for risk identification for people with hearing loss" and was developed to train an embedded system called SSIES (Support System for Identifying Environmental Sounds). The goal of the system is to assist individuals with hearing loss by identifying environmental sounds that signal emergencies or require immediate attention. The dataset is primarily based on selected audio samples from the ESC-50 database, including siren, horn, and baby cry sounds. An additional class, “scream,” was sourced from Freesound.org and self-recordings, as it was not included in the original ESC-50 set. To minimize false positives, a fifth class called “X” was added, consisting of common non-emergency environmental sounds. The audio files were processed to match the constraints of an embedded environment by downsampling to 16 KHz, trimming to 1–1.5 seconds, and applying data augmentation techniques (pitch and speed variations) using the librosa library. This optimized dataset enables the training of lightweight tinyML models capable of real-time emergency sound recognition.