A quick search method for audio signals based on a piecewise linear representation of feature trajectories

This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experiment results indicate significant improvements in search speed. For example the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 seconds from a 200-hour audio database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and Language Processin

Warped K-Means: An algorithm to cluster sequentially-distributed data

[EN] Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the sum of squared error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of- the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.This work has been partially supported by Casmacat (FP7-ICT-2011-7, Project 287576), tranScriptorium (FP7-ICT-2011-9, Project 600707), STraDA (MINECO, TIN2012-37475-0O2-01), and ALMPR (GVA, Prometeo/20091014) projects.Leiva Torres, LA.; Vidal, E. (2013). Warped K-Means: An algorithm to cluster sequentially-distributed data. Information Sciences. 237:196-210. https://doi.org/10.1016/j.ins.2013.02.042S19621023

Movement primitives as a robotic tool to interpret trajectories through learning-by-doing

Articulated movements are fundamental in many human and robotic tasks. While humans can learn and generalise arbitrarily long sequences of movements, and particularly can optimise them to fit the constraints and features of their body, robots are often programmed to execute point-to-point precise but fixed patterns. This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives. Instead of achieving accurate reproductions, the proposed approach aims at interpreting data in an agent-centred fashion, according to an agent's primitive movements. The method improves the accuracy of a reproduction with an incremental process that seeks first a rough approximation by capturing the most essential features of a demonstrated trajectory. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory. The aim is to achieve an agent-centred interpretation and progressive learning that fits in the first place the robots' capability, as opposed to a data-centred decomposition analysis. Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method. In particular, because trajectories are understood and abstracted by means of agent-optimised primitives, the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data. 2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection. This study suggests a novel bio-inspired approach to interpreting, learning and reproducing articulated movements and trajectories. Possible applications include drawing, writing, movement generation, object manipulation, and other tasks where the performance requires human-like interpretation and generalisation capabilities

Individual and Collective Stop-Based Adaptive Trajectory Segmentation

Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspec tive, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use a fixed model or only apply an adaptive model on one dimensional data; it turns out that such methods are not very applicable to build online segmentation for multiple dimensional mobile sensor data (e.g., 3D accelerometer or 11 dimension features like ‘mean’, ‘vari- ance’, ‘covariance’, ‘magnitude’, etc). In this paper, we design an adaptive model for segment- ing real-time accelerometer data from smartphones, which is able to (a) dynamically select suitable dimensions to build a model, and (b) adaptively pick up a proper model. In addition to using the traditional residual-style regression errors to evaluate time series segmentation, we design a rich metric to evaluate mobile data segmentation results, including (1) traditional regression error, (2) Information Retrieval style measurements (i.e., precision, recall, F-measure), and (3) segmentation time delay

Bootstrapping movement primitives from complex trajectories

Lemme A. Bootstrapping movement primitives from complex trajectories. Bielefeld: Bielefeld University; 2014

Trajectory segmentation using dynamic programming

We consider the segmentation of a trajectory into piecewise polynomial parts, or possibly other forms. Segmentation is typically formulated as an optimization problem which trades off model fitting error versus the cost of introducing new segments. Heuristics such as split-and-merge are used to find the best segmentation. We show that for ordered data (eg., single curves or trajectories) the global optimum segmentation can be found by dynamic programming. The approach is easily extended to handle different segment types and top down information about segment boundaries, when available. We show segmentation results for video sequences of a basketball undergoing gravitional and non-gravitaional motion.