Acoustic event classification (AEC) and acoustic event detection (AED) refer to the task of detecting whether specific target events occur in audios. As long short-term memory (LSTM) leads to state-of-the-art results in various speech related tasks, it is employed as a popular solution for AEC as well. This paper focuses on investigating the dynamics of LSTM model on AEC tasks. It includes a detailed analysis on LSTM memory retaining, and a benchmarking of nine different pooling methods on LSTM models using 1.7M generated mixture clips of multiple events with different signal-to-noise ratios. This paper focuses on understanding: 1) utterance-level classification accuracy; 2) sensitivity to event position within an utterance. The analysis is done on the dataset for the detection of rare sound events from DCASE 2017 Challenge. We find max pooling on the prediction level to perform the best among the nine pooling approaches in terms of classification accuracy and insensitivity to event position within an utterance. To authors’ best knowledge, this is the first kind of such work focused on LSTM dynamics for AEC tasks.

Learning trajectory representations is essential in many Location Based Services (LBS) applications. Most traditional methods extract trajectory representations based on manually defined features, while deep learning-based methods can reduce part of the human effort. We propose a Deep Spatiotemporal Trajectory Clustering (DSTC) framework to tackle the Spatiotemporal Trajectory Representation Learning towards the Clustering-friendly space (STRLC) problem. Solving the STRLC problem is not a trivial task because: (1) Defining a uniform token size for datasets with an uneven density of trajectory data is challenging. (2) Measuring the similarity between trajectories spanning time zero in the time dimension is a problem to be solved. (3) It requires first learning a vector that can represent the overall characteristics of spatiotemporal trajectories and then mapping it to a more suitable space for clustering. To tackle these challenges, we first utilize the density-based clustering method to define tokens representing the trajectory points automatically. Then, we use polar coordinates to represent the temporal dimension of trajectories. Additionally, we improve the learned trajectory representations in a clustering-oriented latent space end to end. Experiments conducted on benchmark datasets demonstrate that DSTC achieves better accuracy than existing methods. Moreover, the representations learned from spatiotemporal trajectory data in the real world can be used to identify popular routes during the day.