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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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HPSpeech: Silent Speech Interface for Commodity Headphones
We present HPSpeech, a silent speech interface for commodity headphones. HPSpeech utilizes the existing speakers of the headphones to emit inaudible acoustic signals. The movements of the temporomandibular joint (TMJ) during speech modify the reflection pattern of these signals, which are captured by a microphone positioned inside the headphones. To evaluate the performance of HPSpeech, we tested it on two headphones with a total of 18 participants. The results demonstrated that HPSpeech successfully recognized 8 popular silent speech commands for controlling the music player with an accuracy over 90%. While our tests use modified commodity hardware (both with and without active noise cancellation), our results show that sensing the movement of the TMJ could be as simple as a firmware update for ANC headsets which already include a microphone inside the hear cup. This leaves us to believe that this technique has great potential for rapid deployment in the near future. We further discuss the challenges that need to be addressed before deploying HPSpeech at scale.
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- Award ID(s):
- 2239569
- PAR ID:
- 10492815
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- The ACM International Symposium on Wearable Computing (ISWC'23)
- ISBN:
- 9798400701993
- Page Range / eLocation ID:
- 60 to 65
- Format(s):
- Medium: X
- Location:
- Cancun, Quintana Roo Mexico
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3594738.3611365
