Microphone identification using one-class classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Identifying microphone from noisy recordings by using representative instance one class-classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Student Joseph Germer at microphone at closed-circuit TV demonstration

This is one of a series of photographs accompanying a press release by the New York Trade School announcing the development and demonstration of a new technique in closed-circuit TV. In this work student Joseph Germer asks a question about the demonstration. Original caption reads, "Microphone goes to student Joseph Germer, who asks a question about another phase of the demonstration." Black and white photograph with caption.

A two-microphone noise reduction system for cochlear implant users with nearby microphones. Part I: Signal processing algorithm design and development

Users of cochlear implant systems, that is, of auditory aids which stimulate the auditory nerve at the cochlea electrically, often complain about poor speech understanding in noisy environments. Despite the proven advantages of multimicrophone directional noise reduction systems for conventional hearing aids, only one major manufacturer has so far implemented such a system in a product, presumably because of the added power consumption and size. We present a physically small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. Supporting algorithms, which allow the adjustment of the opening angle and the maximum noise suppression, are proposed and evaluated. A portable real-time device for test in real acoustic environments is presented.

A two-microphone noise reduction system for cochlear implant users with nearby microphones. Part II: Performance Evaluation

Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear) often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signal to noise ratio for rooms with realistic reverberation and more than 18 dB under anechoic conditions. Speech understanding in noise is measured in 6 adult cochlear implant users in a reverberant room, showing average improvements of 7.9–9.6 dB, when compared to a single omnidirectional microphone or 1.3–5.6 dB, when compared to a simple directional two-microphone device. Subjective evaluation in a cafeteria at lunchtime shows a preference of the cochlear implant users for the evaluated device in terms of speech understanding and sound quality.

Multi-microphone recordings (EigenMike32) inside a WFS system (Iosono) through Ambisonics techniques using Matlab

El audio multicanal ha avanzado a pasos agigantados en los últimos años, y no solo en las técnicas de reproducción, sino que en las de capitación también. Por eso en este proyecto se encuentran ambas cosas: un array microfónico, EigenMike32 de MH Acoustics, y un sistema de reproducción con tecnología Wave Field Synthesis, instalado Iosono en la Jade Höchscule Oldenburg. Para enlazar estos dos puntos de la cadena de audio se proponen dos tipos distintos de codificación: la reproducción de la toma horizontal del EigenMike32; y el 3er orden de Ambisonics (High Order Ambisonics, HOA), una técnica de codificación basada en Armónicos Esféricos mediante la cual se simula el campo acústico en vez de simular las distintas fuentes. Ambas se desarrollaron en el entorno Matlab y apoyadas por la colección de scripts de Isophonics llamada Spatial Audio Matlab Toolbox. Para probar éstas se llevaron a cabo una serie de test en los que se las comparó con las grabaciones realizadas a la vez con un Dummy Head, a la que se supone el método más aproximado a nuestro modo de escucha. Estas pruebas incluían otras grabaciones hechas con un Doble MS de Schoeps que se explican en el proyecto “Sally”. La forma de realizar éstas fue, una batería de 4 audios repetida 4 veces para cada una de las situaciones garbadas (una conversación, una clase, una calle y un comedor universitario). Los resultados fueron inesperados, ya que la codificación del tercer orden de HOA quedo por debajo de la valoración Buena, posiblemente debido a la introducción de material hecho para un array tridimensional dentro de uno de 2 dimensiones. Por el otro lado, la codificación que consistía en extraer los micrófonos del plano horizontal se mantuvo en el nivel de Buena en todas las situaciones. Se concluye que HOA debe seguir siendo probado con mayores conocimientos sobre Armónicos Esféricos; mientras que el otro codificador, mucho más sencillo, puede ser usado para situaciones sin mucha complejidad en cuanto a espacialidad. In the last years the multichannel audio has increased in leaps and bounds and not only in the playback techniques, but also in the recording ones. That is the reason of both things being in this project: a microphone array, EigenMike32 from MH Acoustics; and a playback system with Wave Field Synthesis technology, installed by Iosono in Jade Höchscule Oldenburg. To link these two points of the audio chain, 2 different kinds of codification are proposed: the reproduction of the EigenMike32´s horizontal take, and the Ambisonics´ third order (High Order Ambisonics, HOA), a codification technique based in Spherical Harmonics through which the acoustic field is simulated instead of the different sound sources. Both have been developed inside Matlab´s environment and supported by the Isophonics´ scripts collection called Spatial Audio Matlab Toolbox. To test these, a serial of tests were made in which they were compared with recordings made at the time by a Dummy Head, which is supposed to be the closest method to our hearing way. These tests included other recording and codifications made by a Double MS (DMS) from Schoeps which are explained in the project named “3D audio rendering through Ambisonics techniques: from multi-microphone recordings (DMS Schoeps) to a WFS system, through Matlab”. The way to perform the tests was, a collection made of 4 audios repeated 4 times for each recorded situation (a chat, a class, a street and college canteen or Mensa). The results were unexpected, because the HOA´s third order stood under the Well valuation, possibly caused by introducing material made for a tridimensional array inside one made only by 2 dimensions. On the other hand, the codification that consisted of extracting the horizontal plane microphones kept the Well valuation in all the situations. It is concluded that HOA should keep being tested with larger knowledge about Spherical Harmonics; while the other coder, quite simpler, can be used for situations without a lot of complexity with regards to spatiality.

Operator's organizational, direct support and general support maintenance manual : head-microphone kit MK-1697/G (NSN 5965-00-313-8958).

"22 January 1974."

Organizational maintenance repair parts and special tools lists for headset-microphone H 182/PT, (NSN 5965-00-069-8885).

Includes index.

Operator, organizational, DS, GS, and depot maintenance manual : headset-microphone H-182/PT.

"4 March 1968."

Direct support and general support maintenance repair parts and special tools lists (including depot maintenance repair parts and special tools) for headset-microphone H-182/PT, (NSN 5965-00-069-8885).

"7 April 1980."

Organizational, direct support, and general support maintenance repair parts and special tools lists (including depot maintenance repair parts and special tools) : headset-microphone H-101A/U, FSN 5965-627-8382.

Includes index.