This paper presents a new learning strategy for the Sound Event Detection (SED) system to tackle the issues of i) knowledge migration from a pre-trained model to a new target model and ii) learning new sound events without forgetting the previously learned ones without re-training from scratch. In order to migrate the previously learned knowledge from the source model to the target one, a neural adapter is employed on the top of the source model. The source model and the target model are merged via this neural adapter layer. The neural adapter layer facilitates the target model to learn new sound events with minimal training data and maintaining the performance of the previously learned sound events similar to the source model. Our extensive analysis on the DCASE16 and US-SED dataset reveals the effectiveness of the proposed method in transferring knowledge between source and target models without introducing any performance degradation on the previously learned sound events while obtaining a competitive detection performance on the newly learned sound events.

This paper presents a newly developed laboratory paradigm for the laboratory component of signals and systems courses. It involves the implementation of signals and systems algorithms that are written in MATLAB on smartphones using their ARM processors. This smartphone-based approach enables an anywhere-anytime environment for students to conduct signals and systems experiments. The steps involved for running MATLAB codes on smartphones are discussed together with the laboratory experiments that are normally covered in signals and systems courses. It is shown that this paradigm still keeps the programming language for conducting signals and systems experiments to MATLAB while enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts.

This paper presents a real-time unsupervised classification of environmental noise signals without knowing the number of noise classes or clusters. A previously developed online frame-based clustering algorithm is modified by adding feature extraction, a smoothing step and a fading step. The developed unsupervised classification or clustering is examined in terms of purity of clusters and normalized mutual information. The results obtained for actual noise signals exhibit the effectiveness of the introduced unsupervised classification in terms of both classification outcome and computational efficiency.

This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time implementation has been carried out on both the Android and iOS smartphones. The field testing results indicate the superiority of this newly developed app over the previously developed app in terms of classification rates.

This paper presents the steps one needs to take in order to run a signal processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two signal processing application examples from Simulink to smartphone. The application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in signal processing, the approach presented in this paper is of benefit to practicing engineers and signal processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.

This paper presents improvements made to the previously developed noise classification path of the environment-adaptive cochlear implant speech processing pipeline. These improvements consist of the utilization of subband noise features together with a random forest tree classifier. Three commonly encountered noise environments of babble, street, and machinery are considered. The results using actual noise signals indicate that this classification method provides 10% improvement in the overall classification rate compared to the previously developed classification while maintaining the real-time implementation aspect of the entire speech processing pipeline.

This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image data via the Minimum Redundancy Maximum Relevance method, an SVM classifier involving a radial basis kernel function is deployed to separate cancerous tissues from normal tissues. By examining exvivo breast cancer hyperspectral images tagged by a pathologist, the developed classification approach is shown to achieve a sensitivity of about 98% and a specificity of about 99%.