Understanding safety-critical interactions with a home medical device through Distributed Cognition

As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients’ interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients’ interaction strategies, and pointed to design opportunities that could improve patients’ experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a DCog approach effectively helps to understand how safety is achieved or compromised in such a system

Musical instrument classification using non-negative matrix factorization algorithms

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 different musical instrument classes (piano, violin, cello, flute, bassoon and soprano saxophone). In addition, MPEG-7 basic spectral and spectral basis descriptors were considered, providing an effective combination for accurately describing the spectral and timbrai audio characteristics. The audio flies were split using 70% of the available data for training and the remaining 30% for testing. A classifier was developed based on non-negative matrix factorization (NMF) techniques, thus introducing a novel application of NMF. The standard NMF method was examined, as well as its modifications: the local, the sparse, and the discriminant NMF. Experimental results are presented to compare MPEG-7 spectral basis representations with MPEG-7 basic spectral features alongside the various NMF algorithms. The results indicate that the use of the spectrum projection coefficients for feature extraction and the standard NMF classifier yields an accuracy exceeding 95%. ©2006 IEEE

Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in sound classification applications as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set are selected using branchand-bound search, obtaining the most suitable features for classification. A class of classifiers is developed based on the non-negative matrix factorization (NMF). The standard NMF method is examined as well as its modifications: the local, the sparse, and the discriminant NMF. The experimental results compare feature subsets of varying sizes alongside the various NMF algorithms. It has been found that a subset containing the mean and the variance of the first mel-frequency cepstral coefficient and the AudioSpectrumFlatness descriptor along with the means of the AudioSpectrumEnvelope and the AudioSpectrumSpread descriptors when is fed to a standard NMF classifier yields an accuracy exceeding 95%

Applying supervised classifiers based on non-negative matrix factorization to musical instrument classification

In this paper, a new approach for automatic audio classification using non-negative matrix factorization (NMF) is presented. Training is performed onto each audio class individually, whilst during the test phase each test recording is projected onto the several training matrices. Experiments demonstrating the efficiency of the proposed approach were performed for musical instrument classification. Several perceptual features as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes. Subsets of the feature set were selected using branch-and-bound search, in order to obtain the most discriminating features for classification. Several NMF techniques were utilized, namely the standard NMF method, the local NMF, and the sparse NMF. The experiments demonstrate an almost perfect classification (classification error 1.0%), outperforming the state-of-the-art techniques tested for the aforementioned experiment

Audio encoding using Huang and Hilbert transforms

International audienceIn this paper an audio coding scheme based on the Empirical Mode Decomposition (EMD) in association with the Hilbert transform is presented. The audio signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs) by EMD, and the associated instantaneous amplitudes and the instantaneous phases are calculated. The basic principle of the proposed approach consists in encoding the instantaneous amplitudes by linear prediction and the instantaneous phases by scalar quantization. The decoder recovers the original signal from IMFs reconstruction by demodulation and summation. The compression method is applied to different audio signals, and results are compared to MP3 a variable bit rate coder and to wavelet approaches

Non-negative Matrix Factorization: A Survey

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