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Microphone identification addresses the challenge of identifying the microphone signature from the recorded signal. An audio recording system (consisting of microphone, A/D converter, codec, etc.) leaves its unique traces in the recorded signal. Microphone system can be modeled as a linear time invariant system. The impulse response of this system is convoluted with the audio signal which is recorded using “the” microphone. This paper makes an attempt to identify "the" microphone from the frequency response of the microphone. To estimate the frequency response of a microphone, we employ sine sweep method which is independent of speech characteristics. Sinusoidal signals of increasing frequencies are generated, and subsequently we record the audio of each frequency. Detailed evaluation of sine sweep method shows that the frequency response of each microphone is stable. A neural network based classifier is trained to identify the microphone from recorded signal. Results show that the proposed method achieves microphone identification having 100% accuracy.
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Audio Capture Using Piezoelectric Sensors on Vibrating Panel Surfaces
The microphone systems employed by smart devices such as cellphones and tablets require case penetrations that leave them vulnerable to environmental damage. A structural sensor mounted on the back of the display screen can be employed to record audio by capturing the bending vibration signals induced in the display panel by an incident acoustic wave - enabling a functional microphone on a fully sealed device. Distributed piezoelectric sensing elements and low-noise accelerometers were bonded to the surfaces of several different panels and used to record acoustic speech signals. The quality of the recorded signals was assessed using the speech transmission index, and the recordings were transcribed to text using an automatic speech recognition system. Although the quality of the speech signals recorded by the piezoelectric sensors was reduced compared to the quality of speech recorded by the accelerometers, the word-error-rate of each transcription increased only by approximately 2% on average, suggesting that distributed piezoelectric sensors can be used as a low-cost surface microphone for smart devices that employ automatic speech recognition. A method of crosstalk cancellation was also implemented to enable the simultaneous recording and playback of audio signals by an array of piezoelectric elements and evaluated by the measured improvement in the recording’s signal-to-interference ratio.
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- Award ID(s):
- 2104758
- PAR ID:
- 10413999
- Date Published:
- Journal Name:
- 154th Convention of the Audio Engineering Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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