Low-power audio classification for ubiquitous sensor networks (original) (raw)
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Automatic program switching is a future trend for digital hearing aids. To realize this function, a solution for sound environment classification is required. This paper presents an HMM-based sound environment classifier that is imple- mented on a low-power DSP system designed for hearing aid applications. Our experimental results show that it is capable of distinguishing four sound sources (i.e. speech, music, car noise, and babble) with more than 95% accuracy rate and consumes only 0.225 mW of power.
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2013 IEEE International Conference on Electronics, Computing and Communication Technologies, 2013
ABSTRACT In this paper we describe algorithms to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance. We use signal sub-band energy to construct signal-dependent dictionary and matching pursuit algorithms to obtain a sparse representation of a signal. The coefficients of the sparse vector are used as weights to compute weighted features. These features, along with mel frequency cepstral coefficients (MFCC), are used as feature vectors for classification. Experimental results show that the proposed method gives an accuracy as high as 95.6 %, while classifying 14 categories of environmental sound using a gaussian mixture model (GMM).
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Nowadays, digital audio applications are part of our everyday lives. Those applications segment the audio stream into some kind of catalogues audio, and have the corresponding responding to each kind of catalogues audio. Such as in IP Network Camera (IPNC) system, when detected the screaming or window breaking signal, the IPNC system turns the motor towards source generating abnormal sounds. So far, a wide variety of features, being extracted from audio signals in either the temporal or frequency domains. Of these, the Mel-Frequency Cepstral features (MFCC), which are frequency transformed and logarithmically scaled, appear to be universally recognized as the most generally effective for analyzing human voice. The most common classification methods used for this audio class recognition include Gaussian Mixture Models (GMM), K-Nearest Neighbor(k-NN), Neural Networks (NN), support vector machines (SVM), and Hidden Markov Models(HMM) The choice of classification method has been shown to be largely insignificant. In this paper, we took Gaussian Mixture Models (GMM) to classify the audio signal.
Applied Sciences
Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on co...