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.

Poly-Articulate

Poly-Articulate was a series of meditations around the paradoxes of articulation, considered in the heterogeneity of its material, visual, aural and conceptual aspects. Taking form through a collaborative working group of four artists/ designers/writers, the project used different technologies from the most ancient (the voice-box) to the most current (real-time rendering engines). With the utilisation of laser sensors crossing the gallery spaces, 20 digital monitors accompanied by vocal syllables would respond to the viewers? movement. Another piece of technology?a theramin installed below the sculpture of a trachea (inner ear)?mutated the vocal sounds upon its engagement along with a microphone suspended in one of the galleries. The visual and sound aspects of this project made it a compelling spatial experience.

An interactive CDRom accompanied with a text by Justin Clemens was published with this project.

Distributed audio network for speech enhancement in challenging noise backgrounds

This paper presents a new approach to enhance speech based on a distributed microphone network. Each microphone is used to simultaneously classify the input into either one of the noise types or as speech. For enhancing the speech signal a modified spectral subtraction approach is used that utilise the sound information of the entire network to update the noise model even during speech. This improves the reduction of the ambient noise, especially for non-stationary noise types such as street or beach noise. Experiments demonstrate the effectiveness of the proposed system.

Adaptive speech enhancement with varying noise backgrounds

We present a new approach for speech enhancement in the presence of non-stationary and rapidly changing background noise. A distributed microphone system is used to capture the acoustic characteristics of the environment. The input of each microphone is then classified either as speech or one of the predetermined noise types. Further enhancement of speech in respective microphones is carried out using a modified spectral subtraction algorithm that incorporates multiple noise models to quickly adapt to rapid background noise changes. Tests on real world speech captured under diverse conditions demonstrate the effectiveness of this method.

Untitled : live cinema performance

Over the last 30 years Melbourne-based film-maker, writer and academic Dirk de Bruyn has made numerous experimental, documentary and animation films and videos, continuing to maintain a no-budget, independent, self-funded focus for much of his work. De Bruyns distinctive style entails cut-up collages that draw on animation, found footage and fragments of dialogue - dyeing, painting, incising and stencilling the film strip. Live De Bruyn’s anarchic multi projection performances can involve performance, freeform vocal workouts and De Bruyn, ‘bent over and mouthing into a microphone like a demented seagull, totally involved in the relentlessly unravelling collage of home-processed footage’.Penny Webb. Ian Helliwell provided a live electronic soundtrack.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

High-sensitivity acoustic sensors from nanofibre webs

Considerable interest has been devoted to converting mechanical energy into electricity using polymer nanofibres. In particular, piezoelectric nanofibres produced by electrospinning have shown remarkable mechanical energy-to-electricity conversion ability. However, there is little data for the acoustic-to-electric conversion of electrospun nanofibres. Here we show that electrospun piezoelectric nanofibre webs have a strong acoustic-to-electric conversion ability. Using poly(vinylidene fluoride) as a model polymer and a sensor device that transfers sound directly to the nanofibre layer, we show that the sensor devices can detect low-frequency sound with a sensitivity as high as 266 mV Pa(-1). They can precisely distinguish sound waves in low to middle frequency region. These features make them especially suitable for noise detection. Our nanofibre device has more than five times higher sensitivity than a commercial piezoelectric poly(vinylidene fluoride) film device. Electrospun piezoelectric nanofibres may be useful for developing high-performance acoustic sensors.