In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious beh...
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In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD). A microphone was used to record sounds so that we may understand the surrounding environment and video provided ground truth for analysis. The analysis was done on four children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. The goal of this study is to devise novel algorithms to detect these events and open possibility for design of intervention methods. In this paper, we have shown time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events. We observe clusters of pole locations from LPC roots to select candidates and apply pattern matching for classification. We also show novel event detection using online dictionary update method. We show that our proposed method achieves recall rate of 95.5% for SIB, 93.5% for flapping, and 95.5% for rocking which is an increase of approximately 5% compared to flapping events detected by using wrist worn sensors in our previous study.
This paper proposes a linear predictive coding algorithm whereby the quality of the decoded signal is improved to realize a high resolution Digital-to-Analog (D/A) converter and describes the performance of the algori...
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This paper proposes a linear predictive coding algorithm whereby the quality of the decoded signal is improved to realize a high resolution Digital-to-Analog (D/A) converter and describes the performance of the algorithm using computer simulation. The first discussion is the present problems of the digital audio system, one of which is degradation of decoded signal by quantization noise inherent to D/A conversions. Next, some reports published to date for improving the problems, are evaluated to clarify the limitations of their technical applicability. As a result, by regarding the digital signal decoding system as a single function system, the arithmetic means of the input and output groups of signal components are regarded to be equivalent when those signal groups are seen from the viewpoint of their time averages, and proposed here is a digital signal processing algorithm using a linear predictive coding system. Conceptual circuit diagrams to implement the method are shown. The efficacy of this method has been demonstrated by computer simulation, that is, the method increases in effect resolution of the D/A conversion system by at least 1 to 2 bits.
Basically all conventional digital signal processing techniques can be warped by introducing a simple modification to the system. In this paper, the focus is in warped linear predictive coding techniques with applicat...
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Basically all conventional digital signal processing techniques can be warped by introducing a simple modification to the system. In this paper, the focus is in warped linear predictive coding techniques with application to speech and audio coding. The performance of warped LPC is compared with a conventional LPC in listening tests and in terms of technical measures. This is done at various sampling rates as a function of the order of the LPC model.
Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known location...
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ISBN:
(纸本)9781424442959
Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses cross-correlation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.
A great deal of current research in the area of narrowband digital speech compression makes use of the linear Prediction coding (LPC) algorithm to extract the vocal track spectrum. This paper describes a technique tha...
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A great deal of current research in the area of narrowband digital speech compression makes use of the linear Prediction coding (LPC) algorithm to extract the vocal track spectrum. This paper describes a technique that splits the spectrum into two equal halves and performs a piecewise LPC approximation to each half. By taking advantage of the classical benefits of piecewise approximation, the fidelity is expected to be higher than standard LPC. In addition, by making use of under-sampling and spectrum folding, computational requirements are reduced by about 40%. PLPC has been implemented in real time on the CSP-30 computer at the Speech Research and Development Facility of the Communications Security Engineering Office (DCW) at ESD.
EOG is a very effective eye movement recording method, which is not only noninvasive, but also can record any eye movements' information. In order to extract some features come form these information contained in ...
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EOG is a very effective eye movement recording method, which is not only noninvasive, but also can record any eye movements' information. In order to extract some features come form these information contained in EOG automatically, this paper presents a novel EOG feature parameters extraction algorithm. The proposed algorithm includes three main parts. The first is endpoint detection unit which detects the startpoint and endpoint of EOG signals. The second is a preprocessor which consists of band-pass filter, frame blocking procedure and windowing step. The third is feature parameters extracting part which extracts EOG feature by a linear predictive coding model and converts LPC parameters to LPC cepstral (LPCC) coefficients. Finally, the paper gives experimental results.
Methods involved to generate the excitation parameters and gain have been proposed that provide improvement over plain linear predictive coding (LPC) in terms of better speech reconstruction. DCT computation to transm...
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Methods involved to generate the excitation parameters and gain have been proposed that provide improvement over plain linear predictive coding (LPC) in terms of better speech reconstruction. DCT computation to transmit the excitation signal energy in initial few coefficients gives a better reconstruction of innovation signal at the receiver as compared to the primitive method of pitch estimation. Gain computation in terms of the root mean square (RMS) value of the voltage levels for each frame reduces the complexity involved as compared to plain LPC, where gain was calculated in terms of mean square error.
In this paper, we propose a Packet Loss Concealment (PLC) method. Our method is based on Pitch Waveform Replication (PWR) and linear predictive coding (LPC). The estimated packet using LPC is better than that using PW...
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In this paper, we propose a Packet Loss Concealment (PLC) method. Our method is based on Pitch Waveform Replication (PWR) and linear predictive coding (LPC). The estimated packet using LPC is better than that using PWR at a near boundary of the lost packet and the received one. On the other hand, the estimated packet using PWR is better than that using LPC at a distant boundary. Therefore, we combine the two estimated packets by considering the merits and demerits of PWR and LPC. Experimental results show that the proposed method provides better Perceptual Evaluation of Speech Quality (PESQ) scores than the conventional methods. Especially PESQ scores of the proposed method are remarkably excellent in the case of male voice.
This paper introduces a new parametric formulation to linear predictive coding (LPC). Instead of the usual linear prediction filter coefficients, a new scheme similar to the line spectral frequencies (LSF), that is in...
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This paper introduces a new parametric formulation to linear predictive coding (LPC). Instead of the usual linear prediction filter coefficients, a new scheme similar to the line spectral frequencies (LSF), that is insensitive to quantization is proposed. However, in the present case, unlike the LSF scheme, an additional positive weighting factor gives extra freedom for coding. This new representation for LPC modeling is shown to be always stable under quantization.
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