The effectiveness of directional microphone alignment in the Baha Divino

The primary objective of this research study is to determine if off-vertical directional microphone alignments of the Baha Divino significantly impact the Reception Threshold for Sentences (RTS, in dB) using the Hearing in Noise Test (HINT) in a diffuse listening situation.

Subjective preference for a high power hearing aid utilizing a directional microphone

This paper discusses the use of a directional microphone by hearing aid users.

Difference between default telecoil and programmed microphone frequency response in behind-the-ear hearing aids

The notion that the default telecoil (t-coil) frequency response should match the programmed microphone frequency response to provide optimal telephone understanding for hearing aid patients has received little attention. This study addresses differences in the average frequency response of the two transducers in behind-the-ear (BTE) hearing aids.

Proposed design for an ultrasonic optical force-feedback microphone

We discuss a novel approach that would lead to the development of an ultrasonic optical force-feedback measurement microphone.

An optical force-feedback microphone for sensing biophotonics related photoacoustic and photo-thermal phenomena [poster]

We discuss a novel approach to the development of an ultrasonic optical force-feedback measurement microphone suitable for observing biophotonic related photoacoustic and photothermal phenomena at high modulation frequencies and spatial resolution.

Evaluation of directional microphone drift in digital hearing aids

The occurrence of directional microphone drift following hearing aid use has been infrequently examined. This study uses the front-to-side ratio to evaluate changes in directional microphone output from new behind-the-ear hearing aids and following approximately three months of hearing aid use. Results indicate no overall significant differences in the front-to-side ratio between initial and follow-up measurements.

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.