02348nas a2200265   4500000000100000000000100001008004100002260001200043653001000055653001800065653002900083653001900112653002100131653002200152653002300174100001900197700001700216700001700233245010800250856007900358300001000437490000600447520161500453022001402068       2023                            d  c09/202310aAudio10aDeep Learning10aHuman Detection Activity10aHuman Activity10aMachine Learning10aTransfer Learning10aViolence Detection1 aDalila Durães1 aBruno Veloso1 aPaulo Novais00aViolence Detection in Audio: Evaluating the Effectiveness of Deep Learning Models and Data Augmentation  uhttps://www.ijimai.org/journal/sites/default/files/2023-08/ijimai8_3_7.pdf  a72-840 v83 aHuman nature is inherently intertwined with violence, impacting the lives of numerous individuals. Various forms of violence pervade our society, with physical violence being the most prevalent in our daily lives. The study of human actions has gained significant attention in recent years, with audio (captured by microphones) and video (captured by cameras) being the primary means to record instances of violence. While video requires substantial processing capacity and hardware-software performance, audio presents itself as a viable alternative, offering several advantages beyond these technical considerations. Therefore, it is crucial to represent audio data in a manner conducive to accurate classification. In the context of violence in a car, specific datasets dedicated to this domain are not readily available. As a result, we had to create a custom dataset tailored to this particular scenario. The purpose of curating this dataset was to assess whether it could enhance the detection of violence in car-related situations. Due to the imbalanced nature of the dataset, data augmentation techniques were implemented. Existing literature reveals that Deep Learning (DL) algorithms can effectively classify audio, with a commonly used approach involving the conversion of audio into a mel spectrogram image. Based on the results obtained for that dataset, the EfficientNetB1 neural network demonstrated the highest accuracy (95.06%) in detecting violence in audios, closely followed by EfficientNetB0 (94.19%). Conversely, MobileNetV2 proved to be less capable in classifying instances of violence.  a1989-1660