Learning feature extraction for learning from audio data

Today, large collections of digital music plays are available. These audio data are time series which need to be indexed and classified for diverse applications. Indexing and classification differs from time series analysis, in that it generalises several series, whereas time series analysis handles just one series a time. The classification of audio data cannot use similarity measures defined on the raw data, e.g. using time warping, or generalise the shape of the series. The appropriate similarity or generalisation for audio data requires feature extraction before classification can successfully be applied to the transformed data. Methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires to tailor the feature set anew. Hence, we consider the construction of feature extraction methods from elementary operators itself a first learning step. We use a genetic programming approach. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences --

Feature engineering workflow for activity recognition from synchronized inertial measurement units

The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.Comment: Multi-Sensor for Action and Gesture Recognition (MAGR), ACPR 2019 Workshop, Auckland, New Zealan

End-to-End Motion Classification Using Smartwatch Sensor Data

Analysis of smart devices’ sensor data for the classification of human activities has become increasingly targeted by industry and motion research. With the popularization of smartwatches, this data becomes available to everyone. The user’s data from accelerometers and gyroscopes is conventionally analyzed as a multivariate time series to obtain reliable information about the user’s activity at a specific moment. Due to the particular sampling rate instabilities of each device, previous approaches mainly work with feature extraction methods to generalize the information independently of the gear, which requires a lot of time and expertise. To overcome this problem, we present an end-to-end model for activity classification based on convolutional neural networks of different dimensions without extensive feature extraction. The data preprocessing is not computationally intensive and the model can deal with the irregularities of the data. By representing the input as twofold – both, interpolated 1D time series and encoded time series as images with the help of Gramian Angular Summation Fields – the use of computer vision techniques is enabled. In addition, an online prediction is possible and the accuracy is comparable to feature extraction approaches. The model is validated with random 10-fold and leave-one-user-out cross-validation showing improvement regarding the generalization of the task

Feature Extraction for Change-Point Detection using Stationary Subspace Analysis

Detecting changes in high-dimensional time series is difficult because it involves the comparison of probability densities that need to be estimated from finite samples. In this paper, we present the first feature extraction method tailored to change point detection, which is based on an extended version of Stationary Subspace Analysis. We reduce the dimensionality of the data to the most non-stationary directions, which are most informative for detecting state changes in the time series. In extensive simulations on synthetic data we show that the accuracy of three change point detection algorithms is significantly increased by a prior feature extraction step. These findings are confirmed in an application to industrial fault monitoring.Comment: 24 pages, 20 figures, journal preprin

AMP: a new time-frequency feature extraction method for intermittent time-series data

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, these approaches are not always appropriate for intermittent time-series data, where intermittency is characterized by constant values for large periods of time punctuated by sharp and transient increases or decreases in value. Motivated by this, we present aggregation, mode decomposition and projection (AMP) a feature extraction technique particularly suited to intermittent time-series data which contain time-frequency patterns. For our method all individual time-series within a set are combined to form a non-intermittent aggregate. This is decomposed into a set of components which represent the intrinsic time-frequency signals within the data set. Individual time-series can then be _t to these components to obtain a set of numerical features that represent their intrinsic time-frequency patterns. To demonstrate the effectiveness of AMP, we evaluate against the real word task of clustering intermittent time-series data. Using synthetically generated data we show that a clustering approach which uses the features derived from AMP significantly outperforms traditional clustering methods. Our technique is further exemplified on a real world data set where AMP can be used to discover groupings of individuals which correspond to real world sub-populations