Additionally, all the siren files are of only four second duration only. Similarly, the Urban Sound Dataset 14 to access the dataset first, you have to fill the request form and wait for the permission. This is such an extensive process and require large efforts. Even the data is not in the uniform length (AUDIO).
#Mu online 97d sound files download#
For instance, after extensively reviewing the literature 13, 14, 15, 16, 17, 18, the google AudioSet 13 the complete dataset is in video format and for downloading, you have to download videos, then convert all of them into audio. The “ambulance siren” class is not richly interpreted in other papers. This research work describes the development of an audio dataset, a voice that offers a wide range of real-world traffic sounds and emergency vehicle sirens. This research effort aims to develop a specific dataset for the sound of emergency vehicle sirens. It increases the demand for datasets to help control traffic flow and improve emergency response time, especially for fire and health-related incidents. A large dataset is required to train the data-hungry AI algorithms, while the amount of human effort and resources to develop such datasets is enormous, e.g., as stated in 9, 10, 11, there is no clear, detailed, and labelled dataset available for the ambulance or emergency vehicle siren and the road noises.ĭue to the increase in traffic volume, and traffic congestion, roadaccidents have become the norm in the metropolitan cities 12. It creates a massive gap between the dataset’s applications and the researchers. However, there are different domains of life where data sets are scarce, or there is no precise data set available for study. Another large-scale dataset was published 7, 8 that included more than 40 classes of daily life sounds. They have used various audio datasets that are publicly available, providing over a thousand audio clips labelled in multiple categories for different essential sounds, such as clapping, laughter, music, environmental noise, etc. With the availability of large datasets, researchers are making great strides in identifying and understanding audio 1, 2, 3, 4, 5, 6. However, collecting a dataset is a gigantic task, requiring efforts on a larger-scale, time, and resources. In addition, the researchers have implemented countless signal processing and AI techniques on the datasets to achieve their research objectives. AI techniques such as machine learning (ML) and deep learning (DL) for audio event detection and identification are in high demand these days.
Similarly, any audio event-triggered AI algorithm requires a large-scale audio dataset for acoustic detection. Datasets are the key to any AI algorithm for training and decision-making. The technical validity of the dataset is also established.Īrtificial intelligence (AI) is now extensively used in many classification problems including audio classification. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. It also improves emergency response time, especially for fire and health events. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises.
It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. Traffic congestion, accidents, and pollution are becoming a challenge for researchers.
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