HornBase: An audio dataset of car horns in different scenarios and positions.

In recent years, there has been significant growth in the development of Machine Learning (ML) models across various fields, such as image and sound recognition and natural language processing. They need to be trained with a large enough data set, ensuring predictions or results are as accurate as possible. When it comes to models for audio recognition, specifically the detection of car horns, the datasets are generally not built considering the specificities of the different scenarios that may exist in real traffic, being limited to collections of random horns, whose sources are sometimes collected from audio streaming sites. There are benefits associated with a ML model trained on data tailored for horn detection. One notable advantage is the potential implementation of horn detection in smartphones and smartwatches equipped with embedded models to aid hearing-impaired individuals while driving and alert them in potentially hazardous situations, thus promoting social inclusion. Given these considerations, we developed a dataset specifically for car horns. This dataset has 1,080 one-second-long .wav audio files categorized into two classes: horn and not horn. The data collection followed a carefully established protocol designed to encompass different scenarios in a real traffic environment, considering diverse relative positions between the involved vehicles. The protocol defines ten distinct scenarios, incorporating variables within the car receiving the horn, including the presence of internal conversations, music, open or closed windows, engine status (on or off), and whether the car is stationary or in motion. Additionally, there are variations in scenarios associated with the vehicle emitting the horn, such as its relative position-behind, alongside, or in front of the receiving vehicle-and the types of horns used, which may include a short honk, a prolonged one, or a rhythmic pattern of three quick honks. The data collection process started with simultaneous audio recordings on two smartphones positioned inside the receiving vehicle, capturing all scenarios in a single audio file on each device. A 400-meter route was defined in a controlled area, so the audio recordings could be carried out safely. For each established scenario, the route was covered with emissions of different types of horns in distinct positions between the vehicles, and then the route was restarted in the next scenario. After the collection phase, the data preprocessing involved manually cutting each horn sound in multiple one-second windowing profiles, saving them in PCM stereo .wav files with a 16-bit depth and a 44.1 kHz sampling rate. For each horn clipping, a corresponding non-horn clipping in close proximity was performed, ensuring a balanced model. This dataset was designed for utilization in various machine learning algorithms, whether for detecting horns with the binary labels, or classifying different patterns of horns by rearranging labels considering the file nomenclature. In technical validation, classifications were performed using a convolutional neural network trained with spectrograms from the dataset's audio, achieving an average accuracy of 89% across 100 trained models.