Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments

Usually, Sound event detection systems that classify different events from sound data have two main blocks. In the first block, sound events are separated from sound background and in next block, different events are classified. In recent years, this research area has become increasingly popular in a wide range of applications, such as in surveillance and city patterns learning and recognition, mainly when combined with imaging sensors. However, it still poses challenging problems due to existent noise, complexity of the events, poor microphone(s) quality, bad microphone location(s), or events occurring simultaneously. This research aimed to compare accurate signal processing and classification methods to suggest a novel method for detecting sound events from sound background in urban scenes. Using wavelet and Mel-frequency cepstral coefficients, the analysis of the effect of classification methods and minimization of the number of train data are some of the advantages of the proposed method. The proposed methods' application to a standard sounds database led to an accuracy of about 99% in event detection. (c) 2021, Springer Nature Switzerland AG

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance