Audio forensics from acoustic reverberation (original) (raw)

Acoustic Environment Identification and Its Applications to Audio Forensics

IEEE Transactions on Information Forensics and Security, 2013

Binaural approach in acoustic scene simulations in audio forensics

Gunshots, steps, door noises, vehicles, barking dogs… each sound could be a critical proof on the crime scene. Along with acoustic measurements (such as reverberation time, wall isolation, outdoor noise…), binaural miking in acoustic simulations is indeed a powerful instrument for testing a witness' capabilities in hearing sound evidences and check for their direction of arrival on the crime scene. This article is a short introduction to binaural audio and reverberation issues.

Forensic Audio

The adaptive filtering techniques have plenty of applications in any areas where the modeled signals or systems are constantly changing. An adaptive filter is a system whose structure is alterable or adjustable in such a way that its behavior or performance improves through contact with its environment. This chapter focuses on adaptive filtering techniques for forensic audio applications. Multichannel multirate specialized structures are presented as general cases. Five approaches are studied: spectral equalization, adaptive linear prediction (ALP), adaptive noise cancellation (ANC), beamforming and deconvolution or derreverberation. Objective and subjective measurements for the evaluation of intelligibility after speech enhancement are revised.

Audio Recording Location Identification Using Acoustic Environment Signature

IEEE Transactions on Information Forensics and Security, 2000

An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of the room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique based on spectral subtraction to estimate the amount of reverberation and nonlinear filtering based on particle filtering to estimate the background noise. The effectiveness of the proposed method is tested using a data set consisting of speech recordings of two human speakers (one male and one female) made in eight acoustic environments using four commercial grade microphones. Performance of the proposed method is evaluated for various experimental settings such as microphone independent, semi-and full-blind AEI, and robustness to MP3 compression. Performance of the proposed framework is also evaluated using Temporal Derivative-based Spectrum and Mel-Cepstrum (TDSM)-based features. Experimental results show that the proposed method improves AEI performance compared with the direct method (i.e., feature vector is extracted from the audio recording directly). In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack.

A Practical Forensic Method for Enhancing Speech Signals Drowned in Loud Music

Recording audio or video is nowadays easier than ever. Almost every phone can do this task with high quality. This has some serious implications in forensic: almost every dialogue or event can be recorded and used as evidence in trials. The problem is that editing multimedia content has also become a very accessible operation. The advances of editing software make it possible with very convincing results for the untrained audience. Forged recordings could be used in trials. The need for multimedia forensic is imminent. There are two main directions of this field: probe authentication and noise reduction. This paper presents the research activities conducted to extract speech signal masked by loud music. The developed system is based on an adaptive system identification configuration. Various scenarios are studied showing the advantages and disadvantages of the adaptive algorithms that were tested. The influence of the acoustic environment over the performances of the proposed system...

Digital audio forensics

Proceedings of the 9th workshop on Multimedia & security - MM&Sec '07, 2007

In this paper a first approach for digital media forensics is presented to determine the used microphones and the environments of recorded digital audio samples by using known audio steganalysis features. Our first evaluation is based on a limited exemplary test set of 10 different audio reference signals recorded as mono audio data by four microphones in 10 different rooms with 44.1 kHz sampling rate and 16 bit quantisation. Note that, of course, a generalisation of the results cannot be achieved. Motivated by the syntactical and semantical analysis of information and in particular by known audio steganalysis approaches, a first set of specific features are selected for classification to evaluate, whether this first feature set can support correct classifications. The idea was mainly driven by the existing steganalysis features and the question of applicability within a first and limited test set. In the tests presented in this paper, an inter-device analysis with different device characteristics is performed while intra-device evaluations (identical microphone models of the same manufacturer) are not considered. For classification the data mining tool WEKA with K-means as a clustering and Naive Bayes as a classification technique are applied with the goal to evaluate their classification in regard to the classification accuracy on known audio steganalysis features. Our results show, that for our test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments. These first tests show promising results but of course are based on a limited test and training set as well a specific test set generation. Therefore additional and enhanced features with different test set generation strategies are necessary to generalise the findings.

The Use of Time-Scale Analysis in Forensic Acoustical Investigations

WSEAS TRANSACTIONS ON ACOUSTICS AND MUSIC, 2020

The identification and characterization of noise events is one of the most important task in acousticalforensic analysis. In this field it is often fundamental to distinguish, within a complex acoustical framework, thedifferent noise events, especially because, in many cases, the operator cannot be present at the measurements. Itis fundamental to be able to distinguish the atypical or extraneous noise events from the specific ones underinvestigation and know what type of sources make up the noise climate. To this aim is essential to develop a time- frequency analysis technique able to overcame the known limitations of the “traditional” 1/3 of octave frequencyanalyses. In this paper a novel technique, based on multiresolution analysis, has been developed and applied tosome forensic “typical” problems, showing that a suitable choice of the analysis parameters can be able to answerto the main questions of this field

Reverberation time measuring methods

In this paper different well-established methods of reverberation time measurement are compared. Furthermore, the results obtained using these methods are compared to the results provided by some additional methods which could serve as an in situ tool if, for any reason, the reverberation time measurements cannot be carried out using the standardized methods. The methods compared in this paper include the standardized methods (EN ISO 3382:2000), namely the impulse response measured with pink noise, exponential sweep, MLS, but also pistol shots of different calibers, balloon bursts, gated external pink noise, and the B&K filtered burst method. In order to make the comparison, the measurements were performed in four acoustically very different spaces -a rather small and well-damped listening room, a much bigger damped drama theatre, a rather reverberant atrium, and a large and very reverberant shoebox-shaped room. The results were evaluated taking into account to signal-tonoise ratio criterion and special attention has been given to the influence of room modes on measurement results.

Advancing Forensic Analysis of Gunshot Acoustics

Journal of The Audio Engineering Society, 2015

This paper describes our current work to create the apparatus and methodology for scientific and repeatable collection of firearm acoustical properties, including the important direction-dependence of each firearm’s sound field. Gunshot acoustical data is collected for a wide range of firearms using an elevated shooting platform and an elevated spatial array of microphones to allow echo-free directional recordings of each firearm’s muzzle blast. The results of this proposed methodology include a standard procedure for cataloging firearm acoustical characteristics, and a database of acoustical signatures as a function of azimuth for a variety of common firearms and types of ammunition.

Digital audio forensics using background noise

2010

This paper presents a new audio forensics method based on background noise in the audio signals. The traditional speech enhancement algorithms improve the quality of speech signals, however, existing methods leave traces of speech in the removed noise. Estimated noise using these existing methods contains traces of speech signal, also known as leakage signal. Although this speech leakage signal has low SNR, yet it can be perceived easily by listening to the estimated noise signal, it therefore cannot be used for audio forensics applications. For reliable audio authentication, a better noise estimation method is desirable. To achieve this goal, a two-step framework is proposed to estimate the background noise with minimal speech leakage signal. A correlation based similarity measure is then applied to determine the integrity of speech signal. The proposed method has been evaluated for different speech signals recorded in various environments. The results show that it performs better than the existing speech enhancement algorithms with significant improvement in terms of SNR value.

Audio forensics from acoustic reverberation (original) (raw)

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