IKAR Lab 3: Forensic Audio Suite

Professional hardware and software suite for audio forensics.

Since it has been launched in 1992, IKAR Lab has evolved from a sound editor application to the most popular audio forensic lab in the world. Today it is serving experts more than in 350 laboratories in more than 40 countries worldwide.

Audio/speech signal analysis IKAR Lab 3

Overview

IKAR Lab 3. Hardware and Software Suite is designed to conduct forensic analysis of audio recordings.

The suite performs the following tasks:

  • Perform noise reduction of the audio recording and determine the degree of its suitability for identification analysis.
  • Check the authenticity of audio recording.
  • Establishment of the verbatim content of the audio recording and diarization by speakers.
  • Identification of the speaker by voiced speech on the audio recording.

SIS

SIS

 

SIS

Audio forensic software.

SIS is the core component of IKAR Lab 3 Hardware and Software Suite including powerful tools for speech signal examination, excellent capabilities of visible speech display and analysis, segmentation and text transcription, automatic and semi-automatic identification tools, and many other features.

 

SIS Tools:

  • Automatic comparison algorithms;
  • Speech and noise detection;
  • Editing and processing of audio recordings;
  • Expert methods;
  • Signal analysis;
  • Visualization and analysis of audio recordings;
  • Identification wizard;
  • EdiTracker;
  • Diagnostics module;
  • Overall conclusion.

Sound Cleaner

Sound Cleaner. Noise reduction and audio enhancement software


Sound Cleaner

During the examination most of the audio materials require the verbatim content a transcript of a speech recording. Since the audio recordings obtained on-the-fly can be recorded in difficult acoustic conditions and are often unintelligible, noise reduction is carried out as the preparatory stage. For this purpose, IKAR Lab 3 Hardware and Software Suite is optionally equipped with Sound Cleaner software. It includes modern signal processing algorithms and successfully suppresses broadband noise, tonal interference, pulses, performs frequency response correction, equalizes the signal amplitude, etc.

Sound Cleaner is equipped with visualization tools that allow users to detect noises and harmonics that block normal listening. The expert can visually identify the spectrum and signal area whose quality needs to be enhanced.

 

Sound Cleaner Filters:

  • DNN filter;
  • Amplifier;
  • Broadband noise filter;
  • Cellphone noise filter;
  • Click suppressor;
  • Clip restorer;
  • DTMF suppressor;
  • Dynamic range control;
  • Equalizer;
  • Inverse filter;
  • Reference noise canceller;
  • Reference noise suppressor;
  • Reverb suppressor;
  • Automatic filter;
  • Tone suppressor.

Example of DNN filter application

ASR Module

ASR Module

The module provides the text content of the detected speech in 9 different languages.

Text transcription is accompanied by segmentation marking the location of the spoken words. The module automatically marks utterances linking them to the speakers.

The Dictionaries function allows to connect additional transcription dictionaries to the speech recognition module. This is useful when the speech on the audio recording is full of slang.

Video analytics Module

Video analytics Module

SIS offers a video analytics feature that allow to automatically select frames containing objects specified in the target list. The expert can further sort out the results with the search-by-color option using a color palette.

The module filters allow to enhance the image quality by applying noise reduction, changing visualization parameters, deblurring and compensating for defocus.

The module saves the expert's time when searching for a target object on a long video recording and improves the image quality to work with.

STC-H246

 

STC-H246

STC-H246

Audio hardware

To guarantee the high quality of input and output signals, the IKAR Lab 3 suite is equipped with a professional STC-H246 audio hardware device.

STC-H246 is perfect for setting up a workstation for digitising analogue audio recordings. The device is designes to measure parameters and generating electrical signals in the audio frequency range.

Specs

Parameter Value
Sampling rate: 8–200 kHz
Resolution ADC/DAC:  24 bit
Signal-to-noise ratio in the end-to-end channel, in the
frequency band from 20 to 20 kHz:
105 dB
Input/output channel connector types: XLR, RCA, S/PDIF, TRS 6.3
Number of channels: 2
Case: Metal
Size: 111×166×190 mm
OS: Windows 7,10

###1NULL
###2array(5) { [0]=> array(13) { ["fid"]=> string(4) "3004" ["uid"]=> string(1) "1" ["filename"]=> string(12) "aes_39th.pdf" ["filepath"]=> string(40) "files/product/ikarlab2/docs/aes_39th.pdf" ["filemime"]=> string(15) "application/pdf" ["filesize"]=> string(6) "852219" ["status"]=> string(1) "1" ["timestamp"]=> string(10) "1390285062" ["origname"]=> string(12) "aes_39th.pdf" ["list"]=> string(1) "0" ["data"]=> array(1) { ["description"]=> string(102) "gr|Articles|articles|Channel compensation for forensic speaker identification using inverse processing" } ["nid"]=> string(4) "1579" ["view"]=> string(0) "" } [1]=> array(13) { ["fid"]=> string(4) "3005" ["uid"]=> string(1) "1" ["filename"]=> string(18) "f0-_iafpa_2007.pdf" ["filepath"]=> string(46) "files/product/ikarlab2/docs/f0-_iafpa_2007.pdf" ["filemime"]=> string(15) "application/pdf" ["filesize"]=> string(5) "58301" ["status"]=> string(1) "1" ["timestamp"]=> string(10) "1390285083" ["origname"]=> string(18) "f0-_iafpa_2007.pdf" ["list"]=> string(1) "0" ["data"]=> array(1) { ["description"]=> string(83) "gr|Articles|articles|Speaker identification based on the statistical analysis of f0" } ["nid"]=> string(4) "1579" ["view"]=> string(0) "" } [2]=> array(13) { ["fid"]=> string(4) "3006" ["uid"]=> string(1) "1" ["filename"]=> string(21) "ref_channel_aes46.pdf" ["filepath"]=> string(49) "files/product/ikarlab2/docs/ref_channel_aes46.pdf" ["filemime"]=> string(15) "application/pdf" ["filesize"]=> string(6) "759968" ["status"]=> string(1) "1" ["timestamp"]=> string(10) "1390285109" ["origname"]=> string(21) "ref_channel_aes46.pdf" ["list"]=> string(1) "0" ["data"]=> array(1) { ["description"]=> string(118) "gr|Articles|articles|Semi-automated technique for noisy recording enhancement using an independent reference recording" } ["nid"]=> string(4) "1579" ["view"]=> string(0) "" } [3]=> array(13) { ["fid"]=> string(4) "4610" ["uid"]=> string(1) "1" ["filename"]=> string(27) "ikar_lab_3_leaflet_2022.pdf" ["filepath"]=> string(56) "files/product/ikarlab-3/docs/ikar_lab_3_leaflet_2022.pdf" ["filemime"]=> string(15) "application/pdf" ["filesize"]=> string(7) "9880098" ["status"]=> string(1) "1" ["timestamp"]=> string(10) "1660637502" ["origname"]=> string(27) "ikar_lab_3_leaflet_2022.pdf" ["list"]=> string(1) "0" ["data"]=> array(1) { ["description"]=> string(53) "gr|Brochures & White papers|papers|IKAR Lab 3 Leaflet" } ["nid"]=> string(4) "1579" ["view"]=> string(0) "" } [4]=> array(13) { ["fid"]=> string(4) "4611" ["uid"]=> string(1) "1" ["filename"]=> string(29) "ikar_lab_3_octavilla_2022.pdf" ["filepath"]=> string(58) "files/product/ikarlab-3/docs/ikar_lab_3_octavilla_2022.pdf" ["filemime"]=> string(15) "application/pdf" ["filesize"]=> string(8) "10899160" ["status"]=> string(1) "1" ["timestamp"]=> string(10) "1660637565" ["origname"]=> string(29) "ikar_lab_3_octavilla_2022.pdf" ["list"]=> string(1) "0" ["data"]=> array(1) { ["description"]=> string(61) "gr|Brochures & White papers|papers|IKAR Lab 3 Leaflet Spanish" } ["nid"]=> string(4) "1579" ["view"]=> string(0) "" } }