Comparison of Different Feature Types for Acoustic Event Detection System

Kiktová, Eva; Lojka, Martin; Pleva, Matúš; Juhar, Jozef; Čižmár, Anton

doi:10.1007/978-3-642-38559-9_25

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…This adjusted network was validated once again with new data and results of that validation are summarized in Table 3 -the rates of correct answers seem to be very promising. In comparison to some similar work [16], [17], where the rates of correct answers rarely exceed 90%, these results are exceptional.…”

Section: Resultscontrasting

confidence: 83%

Possibilities of Feedforward Multilayer Neural Network Classifier as a Detector of Pest Birds in Vineyards

Doležel

Mariška

Taufer

2015

JERA

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In this paper, the application of artificial neural network clasifier to resolve pest birds in agricultural areas as a part of a comprehensive system of protection against vermin is demonstrated. Firstly, the idea of the whole system is outlined. Then, the method of recognition is described, the process of artificial neural network design is illustrated and the classifier is validated using data gathered in the fields. Eventually, the results are compared to similar works.

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Section: Resultscontrasting

confidence: 83%

Possibilities of Feedforward Multilayer Neural Network Classifier as a Detector of Pest Birds in Vineyards

Doležel

Mariška

Taufer

2015

JERA

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“…Comparison between different acoustic features have been addressed on different datasets and for various speech related activities, e.g. on digits [26], for event detection [27], and on emotional speech classification [28] among others. Not much can be found in the literature on how they perform under variable speaking rates.…”

Section: Related Workmentioning

confidence: 99%

Acoustic Feature Comparison for Different Speaking Rates

Shahrebabaki

Imran

Olfati

et al. 2018

Lecture Notes in Computer Science

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This paper investigates the effect of speaking rate variation on the task of frame classification. This task is indicative of the performance on phoneme and word recognition and is a first step towards designing voice-controlled interfaces. Different speaking rates cause different dynamics. For example, speaking rate variations will cause changes both in formant frequencies and in their transition tracks. A word spoken at normal speed gets recognized more often than the same word spoken by the same speaker at a much faster or slower pace, or viceversa. It is thus imperative to design interfaces which take into account different speaking variabilities. To better incorporate speaker variability into digital devices, we study the effect of a) feature selection and b) the choice of network architecture on variable speaking rates. Four different features are evaluated on multiple configurations of Deep Neural Network (DNN) architectures. The findings show that log Filter-Bank Energies (FBE) outperformed the other acoustic features not only on normal speaking rate but for slow and fast speaking rates as well.

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“…To capture the nature of the acoustic signal, the MFCC (Mel-Frequency Cepstral Coefficients) features were applied [9]. Figure 4 shows the MFCC features for a single typing of a user (i.e.…”

Section: Audio Based Analysismentioning

confidence: 99%

Improving static audio keystroke analysis by score fusion of acoustic and timing data

Pleva

Bours²,

Ondáš

et al. 2017

Multimed Tools Appl

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In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on.

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Comparison of Different Feature Types for Acoustic Event Detection System

Cited by 18 publications

References 4 publications

Possibilities of Feedforward Multilayer Neural Network Classifier as a Detector of Pest Birds in Vineyards

Possibilities of Feedforward Multilayer Neural Network Classifier as a Detector of Pest Birds in Vineyards

Acoustic Feature Comparison for Different Speaking Rates

Improving static audio keystroke analysis by score fusion of acoustic and timing data

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