@Article{GattoFors:2021:AuMaLe,
               author = "Gatto, Rubens Cruz and Forster, Carlos Henrique Quartucci",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
                title = "Audio-Based Machine Learning Model for Traffic Congestion 
                         Detection",
              journal = "IEEE Transactions on Intelligent Transportation Systems",
                 year = "2021",
               volume = "22",
               number = "11",
                pages = "7200--7207",
                month = "Nov.",
             keywords = "audio signal processing, machine learning, Traffic.",
             abstract = "The present work approaches intelligent traffic evaluation and 
                         congestion detection using sound sensors and machine learning. For 
                         this, two important problems are addressed: traffic condition 
                         assessment from audio data, and analysis of audio under 
                         uncontrolled environments. By modeling the traffic parameters and 
                         the sound generation from passing vehicles and using the produced 
                         audio as a source of data for learning the traffic audio patterns, 
                         we provide a solution that copes with the time, the cost and the 
                         constraints inherent to the activity of traffic monitoring. 
                         External noise sources were introduced to produce more realistic 
                         acoustic scenes and to verify the robustness of the methods 
                         presented. Audio-based monitoring becomes a simple and low-cost 
                         option, comparing to other methods based on detector loops, or 
                         GPS, and as good as camera-based solutions, without some of the 
                         common problems of image-based monitoring, such as occlusions and 
                         light conditions. The approach is evaluated with data from audio 
                         analysis of traffic registered in locations around the city of 
                         S{\~a}o Jose dos Campos, Brazil, and audio files from places 
                         around the world, downloaded from YouTube. Its validation shows 
                         the feasibility of traffic automatic audio monitoring as well as 
                         using machine learning algorithms to recognize audio patterns 
                         under noisy environments.",
                  doi = "10.1109/TITS.2020.3003111",
                  url = "http://dx.doi.org/10.1109/TITS.2020.3003111",
                 issn = "1524-9050",
             language = "en",
           targetfile = "gatto_audio.pdf",
        urlaccessdate = "13 maio 2024"
}