1,543 research outputs found
A Spectral Subtraction Based Algorithm for Real-time Noise Cancellation with Application to Gunshot Acoustics
This paper introduces an improved spectral subtraction based algorithm for real-time noise cancellation, applied to gunshot acoustical signals. The derivation is based on the fact that, in practice, relatively long periods without gunshot signals occur and the background noise can be modeled as being short-time stationary and uncorrelated to the impulsive gunshot signals. Moreover, gunshot signals, in general, have a spiky autocorrelation while typical vehicle noise, or related, is periodic and exhibits a wider autocorrelation. The Spectral Suppression algorithm is applied using the pre-filtering approach, as opposed to post-filtering which requires a priori knowledge of the direction of arrival of the signals of interest, namely, the Muzzle blast and the Shockwave. The results presented in this work are based on a dataset generated by combining signals from real gunshots and real vehicle noise
Learning sound representations using trainable COPE feature extractors
Sound analysis research has mainly been focused on speech and music
processing. The deployed methodologies are not suitable for analysis of sounds
with varying background noise, in many cases with very low signal-to-noise
ratio (SNR). In this paper, we present a method for the detection of patterns
of interest in audio signals. We propose novel trainable feature extractors,
which we call COPE (Combination of Peaks of Energy). The structure of a COPE
feature extractor is determined using a single prototype sound pattern in an
automatic configuration process, which is a type of representation learning. We
construct a set of COPE feature extractors, configured on a number of training
patterns. Then we take their responses to build feature vectors that we use in
combination with a classifier to detect and classify patterns of interest in
audio signals. We carried out experiments on four public data sets: MIVIA audio
events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that
we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on
the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund)
demonstrate the effectiveness of the proposed method and are higher than the
ones obtained by other existing approaches. The COPE feature extractors have
high robustness to variations of SNR. Real-time performance is achieved even
when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio
Fast Blind Audio Copy-Move Detection and Localization Using Local Feature Tensors in Noise
The increasing availability of audio editing software altering digital audios
and their ease of use allows create forgeries at low cost. A copy-move forgery
(CMF) is one of easiest and popular audio forgeries, which created by copying
and pasting audio segments within the same audio, and potentially
post-processing it. Three main approaches to audio copy-move detection exist
nowadays: samples/frames comparison, acoustic features coherence searching and
dynamic time warping. But these approaches will suffer from computational
complexity and/or sensitive to noise and post-processing. In this paper, we
propose a new local feature tensors-based copy-move detection algorithm that
can be applied to transformed duplicates detection and localization problem to
a special locality sensitive hash like procedure. The experimental results with
massive online real-time audios datasets reveal that the proposed technique
effectively determines and locating copy-move forgeries even on a forged speech
segment are as short as fractional second. This method is also computational
efficient and robust against the audios processed with severe nonlinear
transformation, such as resampling, filtering, jsittering, compression and
cropping, even contaminated with background noise and music. Hence, the
proposed technique provides an efficient and reliable way of copy-move forgery
detection that increases the credibility of audio in practical forensics
application
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