Advanced features and feature selection methods for vibration and audio signal classification

An adequate feature set plays a key role in many signal classification and recognition applications. This is a challenging problem due to the nonlinearity and nonstationary characteristics of real world signals, such as engine acoustic/vibration data, environmental sounds, speech signals and music instrument sounds. Some of traditional features such as the Mel Frequency Cepstral Coefficients (MFCC) may not offer good performance. Other features such as those based on the Matching Pursuit (MP) decomposition may perform better, yet their complexity is very high. In this research, we consider a new feature set that can be easily generated in the model-based signal compression process, known as the Code Excited Linear Prediction (CELP) features. The CELP-based coding algorithm and its variants have been widely used to encode speech and low-bit-rate audio signals. In this research, we examine two applications based on CELP-based features.;First, we present a new approach to engine fault detection and diagnosis based on acoustic and vibration sensor data with MFCC and CELP features. Through proper algorithmic adaptation to the specifics of the dataset, the fault conditions of a damaged blade and a bearing failure can, with high probability, be autonomously discovered and identified. The conducted experiments will show that CELP features, although generally used in speech applications, are particularly well suited to this problem, in terms of both compactness and detection specificity. Furthermore, the issue of automatic fault detection with different levels of decision resolution is addressed. The low prediction error coupled with ease of hardware implementation makes this proposed method an attractive alternative to manual maintenance.;Next, we propose the use of CELP-based features to enhance the performance of the environmental sound recognition (ESR) problem. Traditionally, MFCC features have been used for the recognition of structured data like speech and music. However, their performance for the ESR problem is limited. An audio signal can be well preserved by its highly compressed CELP bit streams, which motivates us to study the CELP-based features for the audio scene recognition problem. We present a way to extract a set of features from the CELP bit streams and compare the performance of ESR using different feature sets with the Bayesian network classifier. It is shown by experimental results that the CELP-based features outperform the MFCC features in the ESR problem by a significant margin and the integrated MFCC and CELP-based feature set can even reach a correct classification rate of 95.2% using the Bayesian network classifier.;CELP-based features may not be suitable for wideband audio signals such as music signals. To address this problem, we would like to add other new features. One idea is to perform real-time fundamental frequency estimation using a modified Hilbert-Huang transform (HHT), as studied in the last part of this proposal. HHT is a non-linear transform which is suitable for the analysis of non-stationary AM/FM like data. However, the application of HHT directly to music signals encounters several problems. In this research, we modify HHT so that it can be tailored to the short-window pitch analysis. It is shown by experimental results that the proposed HHT method performs significantly better than several benchmark schemes.;Finally, for the ESR application with large number of classes, more features are needed in order to maintain the classification performance. On the other hand, more data are desired to avoid the over-fit problem. These two become contradicting requirements. We propose two methods to resolve the contradiction. They are the content-adaptive feature selection method and the context-based feature selection method. The content-adaptive feature selection method selects different features used for different testing samples according to their statistics. The context-based feature selection method eliminates the loading of the classifier by adding the context stage as preprocessing layer. As a result, we can dramatically decrease the number of features used and adaptively select a good subset of features.